Results publication

workflowr

• Summary
• Checks
• Past versions

Last updated: 2022-08-11

Checks: 7 0

Knit directory: emlr_obs_analysis/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20210412)

The command set.seed(20210412) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 77719db

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 77719db. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    data/
    Ignored:    output/other/
    Ignored:    output/publication/

Unstaged changes:
    Modified:   code/Workflowr_project_managment.R

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/results_publication.Rmd) and HTML (docs/results_publication.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	77719db	jens-daniel-mueller	2022-08-11	rebuild with 5 basin separation as standard case
html	318fefe	jens-daniel-mueller	2022-08-11	Build site.
Rmd	60dae4b	jens-daniel-mueller	2022-08-11	delta dcant budget analysis
html	5b77c4f	jens-daniel-mueller	2022-08-10	Build site.
Rmd	c25333a	jens-daniel-mueller	2022-08-10	bias sections and maps added
html	9c3be27	jens-daniel-mueller	2022-08-09	Build site.
Rmd	a940880	jens-daniel-mueller	2022-08-09	improve delta dcant bias assesment
html	a691b29	jens-daniel-mueller	2022-08-09	Build site.
Rmd	d136a86	jens-daniel-mueller	2022-08-09	include delta dcant bias assesment
html	910f96e	jens-daniel-mueller	2022-08-08	Build site.
Rmd	e064958	jens-daniel-mueller	2022-08-08	include bias assesment
html	e99640e	jens-daniel-mueller	2022-07-29	Build site.
html	f68fcfa	jens-daniel-mueller	2022-07-26	Build site.
Rmd	abec0de	jens-daniel-mueller	2022-07-26	changed color scale back
html	f2615fb	jens-daniel-mueller	2022-07-26	Build site.
Rmd	8da318e	jens-daniel-mueller	2022-07-26	changed color scale
html	99a82db	jens-daniel-mueller	2022-07-25	Build site.
Rmd	1d6985d	jens-daniel-mueller	2022-07-25	stat analysis of layer budgets and CIs
html	4af29f9	jens-daniel-mueller	2022-07-24	Build site.
Rmd	b2c4ff6	jens-daniel-mueller	2022-07-24	revised GCB plot
html	e2adac6	jens-daniel-mueller	2022-07-22	Build site.
Rmd	a9c8af4	jens-daniel-mueller	2022-07-22	revised section plots and tables
html	9bbc6a4	jens-daniel-mueller	2022-07-20	Build site.
Rmd	f937de2	jens-daniel-mueller	2022-07-20	revised plots
html	0c6db30	jens-daniel-mueller	2022-07-20	Build site.
Rmd	a42e52d	jens-daniel-mueller	2022-07-20	revised plots
html	b18b250	jens-daniel-mueller	2022-07-20	Build site.
Rmd	36691bf	jens-daniel-mueller	2022-07-20	revised plots
html	a80b59b	jens-daniel-mueller	2022-07-20	Build site.
Rmd	f9c0545	jens-daniel-mueller	2022-07-20	revised budget stats
html	fea41c1	jens-daniel-mueller	2022-07-20	Build site.
Rmd	b27e42f	jens-daniel-mueller	2022-07-20	rerun with corrected Canyon-B talk gap-filling in standard case
html	9ce772d	jens-daniel-mueller	2022-07-19	Build site.
Rmd	aaf37cf	jens-daniel-mueller	2022-07-19	coverage maps with gap filling
html	d803308	jens-daniel-mueller	2022-07-19	Build site.
Rmd	933285c	jens-daniel-mueller	2022-07-19	revised plots
html	b1f7ab3	jens-daniel-mueller	2022-07-18	Build site.
Rmd	ab5220b	jens-daniel-mueller	2022-07-18	revised budget over atm pCO2 plot
html	d2ae54c	jens-daniel-mueller	2022-07-18	Build site.
Rmd	85f482c	jens-daniel-mueller	2022-07-18	include scaled Sabine data for budgets
html	2695085	jens-daniel-mueller	2022-07-17	Build site.
Rmd	8624c93	jens-daniel-mueller	2022-07-17	use global output from MLR basins
html	535196a	jens-daniel-mueller	2022-07-17	Build site.
html	d20faeb	jens-daniel-mueller	2022-07-17	Build site.
Rmd	b6ef86b	jens-daniel-mueller	2022-07-17	revised budget plots
html	0160c40	jens-daniel-mueller	2022-07-16	Build site.
Rmd	d2b1090	jens-daniel-mueller	2022-07-16	cleaned code
html	efa414b	jens-daniel-mueller	2022-07-16	Build site.
Rmd	e84b169	jens-daniel-mueller	2022-07-16	added bias global analysis
html	7267b08	jens-daniel-mueller	2022-07-16	Build site.
Rmd	b91f619	jens-daniel-mueller	2022-07-16	analysed dcant budget stats
html	08c00b4	jens-daniel-mueller	2022-07-16	Build site.
html	692c937	jens-daniel-mueller	2022-07-16	Build site.
html	afb27ad	jens-daniel-mueller	2022-07-15	Build site.
Rmd	53e63d5	jens-daniel-mueller	2022-07-15	plot no surface data equi
html	b492b46	jens-daniel-mueller	2022-07-15	Build site.
Rmd	31f29fe	jens-daniel-mueller	2022-07-15	plot decadal sink trends as boxplots
html	bd24a0f	jens-daniel-mueller	2022-07-15	Build site.
Rmd	efbb1dc	jens-daniel-mueller	2022-07-15	include no surface equi cases
html	022fd60	jens-daniel-mueller	2022-07-14	Build site.
Rmd	5a9e419	jens-daniel-mueller	2022-07-14	implemented beta budget analysis
Rmd	d2092c1	jens-daniel-mueller	2022-07-14	implemented beta budget analysis
html	f1d7f80	jens-daniel-mueller	2022-07-13	Build site.
Rmd	6811ae4	jens-daniel-mueller	2022-07-13	refined profil plots
html	ea46812	jens-daniel-mueller	2022-07-13	Build site.
Rmd	defdcfe	jens-daniel-mueller	2022-07-13	additional analyis
html	17cd1d1	jens-daniel-mueller	2022-07-13	Build site.
Rmd	1bf1708	jens-daniel-mueller	2022-07-13	rerun reoccupation
html	26e9496	jens-daniel-mueller	2022-07-12	Build site.
Rmd	5d121f0	jens-daniel-mueller	2022-07-12	applied dcant scaling and improved ensemble member analysis
html	8fb595c	jens-daniel-mueller	2022-07-12	Build site.
Rmd	e2d97ef	jens-daniel-mueller	2022-07-12	excluded no adjustment from ensemble
html	003b161	jens-daniel-mueller	2022-07-12	Build site.
Rmd	54a0d14	jens-daniel-mueller	2022-07-12	added cruise based adjustment
html	b44c72a	jens-daniel-mueller	2022-07-03	Build site.
html	37f56b3	jens-daniel-mueller	2022-07-01	Build site.
Rmd	8c4a9f8	jens-daniel-mueller	2022-07-01	added basin separation analysis
html	232909e	jens-daniel-mueller	2022-07-01	Build site.
Rmd	7f85db3	jens-daniel-mueller	2022-07-01	added basin separation analysis
html	df21d31	jens-daniel-mueller	2022-07-01	Build site.
Rmd	2bbfba0	jens-daniel-mueller	2022-07-01	rebuild
html	6be73e0	jens-daniel-mueller	2022-06-30	Build site.
Rmd	6e173bf	jens-daniel-mueller	2022-06-30	updated regional budget plots
html	6e173bf	jens-daniel-mueller	2022-06-30	updated regional budget plots
html	8ab4a87	jens-daniel-mueller	2022-06-29	Build site.
Rmd	d8c7cb9	jens-daniel-mueller	2022-06-29	ensemble with unadjusted data
html	7629c78	jens-daniel-mueller	2022-06-29	Build site.
Rmd	4758911	jens-daniel-mueller	2022-06-29	ensemble without unadjusted data
html	f6786c8	jens-daniel-mueller	2022-06-29	Build site.
Rmd	6f694a1	jens-daniel-mueller	2022-06-29	included Cstar N in ensemble
html	f09080e	jens-daniel-mueller	2022-06-28	Build site.
Rmd	d457610	jens-daniel-mueller	2022-06-28	save figures for publication
html	ee30748	jens-daniel-mueller	2022-06-28	Build site.
Rmd	fe42644	jens-daniel-mueller	2022-06-28	GCB emissions ratio included
html	9393c07	jens-daniel-mueller	2022-06-28	Build site.
Rmd	2a3cf97	jens-daniel-mueller	2022-06-28	included basin-hemisphere bias, and bias contributions
html	0825298	jens-daniel-mueller	2022-06-28	Build site.
Rmd	d35ddb1	jens-daniel-mueller	2022-06-28	included GCB ocean sink data as boxplot
html	a13a7cf	jens-daniel-mueller	2022-06-28	Build site.
html	fb59a6f	jens-daniel-mueller	2022-06-27	Build site.
Rmd	7ff568c	jens-daniel-mueller	2022-06-27	included GCB ocean sink data
html	a26a21d	jens-daniel-mueller	2022-06-27	Build site.
Rmd	9425709	jens-daniel-mueller	2022-06-27	scaled sabine 2004 to full area
html	748aa43	jens-daniel-mueller	2022-06-27	Build site.
Rmd	82d5793	jens-daniel-mueller	2022-06-27	ensemble scatter plot
html	457e640	jens-daniel-mueller	2022-06-27	Build site.
Rmd	426c5bf	jens-daniel-mueller	2022-06-27	cleaned read-in section
html	16dc3af	jens-daniel-mueller	2022-06-27	Build site.
Rmd	dd2063a	jens-daniel-mueller	2022-06-27	ensemble member analysis added
html	87e9eb8	jens-daniel-mueller	2022-06-27	Build site.
Rmd	09a3348	jens-daniel-mueller	2022-06-27	1 as standard case and new ensemble members
html	b52b159	jens-daniel-mueller	2022-06-27	Build site.
html	09b0780	jens-daniel-mueller	2022-05-24	Build site.
html	25da2fb	jens-daniel-mueller	2022-05-24	Build site.
html	1d73ec9	jens-daniel-mueller	2022-05-16	Build site.
Rmd	e117b7b	jens-daniel-mueller	2022-05-16	rerun w/o data adjustments
html	2ffbdda	jens-daniel-mueller	2022-05-16	Build site.
Rmd	6fc8438	jens-daniel-mueller	2022-05-16	plot individual zonal sections
html	c71227f	jens-daniel-mueller	2022-05-16	Build site.
Rmd	55e9ac6	jens-daniel-mueller	2022-05-16	plot individual zonal sections
html	3c1100c	jens-daniel-mueller	2022-05-16	Build site.
Rmd	fdb111d	jens-daniel-mueller	2022-05-16	plot individual zonal sections
html	2ca0109	jens-daniel-mueller	2022-05-02	Build site.
Rmd	63d51bd	jens-daniel-mueller	2022-05-02	rerun with adjusted data
html	dcf2eaf	jens-daniel-mueller	2022-05-02	Build site.
Rmd	a3a2cf9	jens-daniel-mueller	2022-05-02	IO sections seperate
html	eff4fd7	jens-daniel-mueller	2022-05-02	Build site.
Rmd	39e920a	jens-daniel-mueller	2022-05-02	IO sections seperate
html	08607eb	jens-daniel-mueller	2022-05-02	Build site.
Rmd	0791c1b	jens-daniel-mueller	2022-05-02	modified plots
html	b018a9a	jens-daniel-mueller	2022-04-29	Build site.
Rmd	02ede93	jens-daniel-mueller	2022-04-29	standard case uncorrected data
html	e09320d	jens-daniel-mueller	2022-04-12	Build site.
Rmd	0dec180	jens-daniel-mueller	2022-04-12	3 data adjustment procedures implemented
html	8dca96a	jens-daniel-mueller	2022-04-12	Build site.
Rmd	e5e9288	jens-daniel-mueller	2022-04-12	3 data adjustment procedures implemented
html	2f20ea6	jens-daniel-mueller	2022-04-11	Build site.
html	209c9b6	jens-daniel-mueller	2022-04-10	Build site.
Rmd	537aff7	jens-daniel-mueller	2022-04-10	no data adjustment implemented
html	acad2e2	jens-daniel-mueller	2022-04-09	Build site.
html	3d81135	jens-daniel-mueller	2022-04-07	Build site.
html	0f5d372	jens-daniel-mueller	2022-04-04	Build site.
Rmd	ac5121f	jens-daniel-mueller	2022-04-04	added zonal mean beta distribution new figure
html	1b5a309	jens-daniel-mueller	2022-04-04	Build site.
Rmd	8b96a9d	jens-daniel-mueller	2022-04-04	added zonal mean beta distribution new figure
html	a74e341	jens-daniel-mueller	2022-04-04	Build site.
Rmd	c0432be	jens-daniel-mueller	2022-04-04	added zonal mean beta distribution ensemble sd
html	b599680	jens-daniel-mueller	2022-04-04	Build site.
Rmd	e406c39	jens-daniel-mueller	2022-04-04	added zonal mean beta distribution
html	ca7e590	jens-daniel-mueller	2022-03-22	Build site.
Rmd	8cb4b78	jens-daniel-mueller	2022-03-22	use 1800 as tref for sabine estimates
html	5a6be34	jens-daniel-mueller	2022-03-22	Build site.
Rmd	cee203c	jens-daniel-mueller	2022-03-22	rerun with NP 2021 talk correction
html	bd9e11d	jens-daniel-mueller	2022-03-22	Build site.
html	2501978	jens-daniel-mueller	2022-03-21	Build site.
Rmd	9e12898	jens-daniel-mueller	2022-03-21	use 1800 as tref for sabine estimates
html	c3a6238	jens-daniel-mueller	2022-03-08	Build site.
Rmd	775eb4f	jens-daniel-mueller	2022-03-08	moving eras analysis implemented
html	094bfa0	jens-daniel-mueller	2022-02-18	Build site.
Rmd	fa258cc	jens-daniel-mueller	2022-02-18	updated plots
html	ba2d62e	jens-daniel-mueller	2022-02-17	Build site.
Rmd	5420131	jens-daniel-mueller	2022-02-17	added Seaflux data
html	192504c	jens-daniel-mueller	2022-02-17	Build site.
Rmd	2394302	jens-daniel-mueller	2022-02-17	added Seaflux data
html	251c7cf	jens-daniel-mueller	2022-02-17	Build site.
Rmd	9ea0d8c	jens-daniel-mueller	2022-02-17	adapted beta factor
html	565224d	jens-daniel-mueller	2022-02-17	Build site.
Rmd	33422e3	jens-daniel-mueller	2022-02-17	scaled budgets to global coverage
html	2116dd3	jens-daniel-mueller	2022-02-09	Build site.
Rmd	e281bf3	jens-daniel-mueller	2022-02-09	updated plots
html	6fe70a1	jens-daniel-mueller	2022-02-05	Build site.
Rmd	8955c85	jens-daniel-mueller	2022-02-05	cleaned plots
html	a6b33aa	jens-daniel-mueller	2022-02-04	Build site.
Rmd	73efef8	jens-daniel-mueller	2022-02-04	atm co2 time series plotted, uncertainties revised
html	4b48475	jens-daniel-mueller	2022-02-04	Build site.
Rmd	6afd7b1	jens-daniel-mueller	2022-02-04	calculated uncertainty of basin budget changes
html	fec5a1e	jens-daniel-mueller	2022-02-04	Build site.
Rmd	325da3a	jens-daniel-mueller	2022-02-04	calculated uncertainty of basin budget changes
html	d2191ad	jens-daniel-mueller	2022-02-04	Build site.
Rmd	79a54fe	jens-daniel-mueller	2022-02-04	new coaverage map
html	4c5b079	jens-daniel-mueller	2022-02-03	Build site.
Rmd	66d89a9	jens-daniel-mueller	2022-02-03	added surface flux model products
html	4077397	jens-daniel-mueller	2022-02-03	Build site.
Rmd	c0c3be1	jens-daniel-mueller	2022-02-03	added surface flux products
html	0d0f790	jens-daniel-mueller	2022-02-02	Build site.
Rmd	049346f	jens-daniel-mueller	2022-02-02	shifted year labels
html	4673df5	jens-daniel-mueller	2022-02-02	Build site.
Rmd	1185397	jens-daniel-mueller	2022-02-02	rearranged plots
html	60727e6	jens-daniel-mueller	2022-02-02	Build site.
Rmd	db5308b	jens-daniel-mueller	2022-02-02	rearranged plots
html	c7b4984	jens-daniel-mueller	2022-02-02	Build site.
Rmd	a3d8469	jens-daniel-mueller	2022-02-02	ensemble uncertainties in global buget
html	7fb28a2	jens-daniel-mueller	2022-02-02	Build site.
Rmd	a332fe6	jens-daniel-mueller	2022-02-02	ensemble uncertainties in time series
html	49097e8	jens-daniel-mueller	2022-02-02	Build site.
Rmd	f98c33b	jens-daniel-mueller	2022-02-02	incl ensemble uncertainties in plot
html	fe11bfd	jens-daniel-mueller	2022-02-02	Build site.
Rmd	d3f7f05	jens-daniel-mueller	2022-02-02	incl ensemble uncertainties
html	fa46251	jens-daniel-mueller	2022-02-02	Build site.
Rmd	33feacf	jens-daniel-mueller	2022-02-02	incl sabine budgets
html	7655085	jens-daniel-mueller	2022-02-02	Build site.
Rmd	09dd555	jens-daniel-mueller	2022-02-02	incl sabine budgets
html	226d67d	jens-daniel-mueller	2022-02-02	Build site.
Rmd	ed056e2	jens-daniel-mueller	2022-02-02	incl sabine budgets
html	ed903f7	jens-daniel-mueller	2022-02-02	Build site.
Rmd	9d11695	jens-daniel-mueller	2022-02-02	scaled budget to atm pco2 increase
html	32e9682	jens-daniel-mueller	2022-02-02	Build site.
Rmd	5cc5572	jens-daniel-mueller	2022-02-02	included Sabine column inventory as reference
html	913e42f	jens-daniel-mueller	2022-02-01	Build site.
Rmd	c4b2de9	jens-daniel-mueller	2022-02-01	updated profile plots
html	189de95	jens-daniel-mueller	2022-02-01	Build site.
Rmd	b0c17bc	jens-daniel-mueller	2022-02-01	updated profile plots
html	ab001eb	jens-daniel-mueller	2022-01-31	Build site.
Rmd	ccf6723	jens-daniel-mueller	2022-01-31	filled step plot for layer budgets
html	d2ae5fe	jens-daniel-mueller	2022-01-31	Build site.
Rmd	278523c	jens-daniel-mueller	2022-01-31	step plot for layer budgets
html	b62308d	jens-daniel-mueller	2022-01-31	Build site.
Rmd	714c5cc	jens-daniel-mueller	2022-01-31	step plot for layer budgets
html	ec7fe7e	jens-daniel-mueller	2022-01-31	Build site.
Rmd	5c948ae	jens-daniel-mueller	2022-01-31	added time series vs atm pco2
html	de557de	jens-daniel-mueller	2022-01-28	Build site.
html	5f2aed0	jens-daniel-mueller	2022-01-27	Build site.
Rmd	54c9e26	jens-daniel-mueller	2022-01-27	added layer budget profiles
html	eccd82b	jens-daniel-mueller	2022-01-26	Build site.
Rmd	c5577d3	jens-daniel-mueller	2022-01-26	added meand sd to offset mean concentrations profiles
html	c6fe495	jens-daniel-mueller	2022-01-26	Build site.
Rmd	e0e7974	jens-daniel-mueller	2022-01-26	added offset mean concentrations profiles
html	9753eb8	jens-daniel-mueller	2022-01-26	Build site.
html	b1d7720	jens-daniel-mueller	2022-01-21	Build site.
Rmd	0210ed5	jens-daniel-mueller	2022-01-21	added mean concentrations profiles per 5 basins
html	d6b399a	jens-daniel-mueller	2022-01-21	Build site.
Rmd	da17a07	jens-daniel-mueller	2022-01-21	added mean concentrations profiles
html	c499be8	jens-daniel-mueller	2022-01-21	Build site.
Rmd	d941871	jens-daniel-mueller	2022-01-21	run color map test
html	e572075	jens-daniel-mueller	2022-01-21	Build site.
Rmd	99b6c92	jens-daniel-mueller	2022-01-21	run color map test
html	4fe7150	jens-daniel-mueller	2022-01-21	Build site.
Rmd	0379e99	jens-daniel-mueller	2022-01-21	script cleaning
html	49b41cf	jens-daniel-mueller	2022-01-21	Build site.
Rmd	2c82651	jens-daniel-mueller	2022-01-21	added map of scaled absolute change
html	c0807e8	jens-daniel-mueller	2022-01-21	Build site.
Rmd	5dd3d7a	jens-daniel-mueller	2022-01-21	added map of scaled relative change
html	22b421f	jens-daniel-mueller	2022-01-21	Build site.
Rmd	2c3fa75	jens-daniel-mueller	2022-01-21	cleaned alluvial plots
html	1a35f1f	jens-daniel-mueller	2022-01-20	Build site.
Rmd	e58f510	jens-daniel-mueller	2022-01-20	added relative changes to alluvial plots
html	b503ae1	jens-daniel-mueller	2022-01-20	Build site.
Rmd	2eb2567	jens-daniel-mueller	2022-01-20	added relative changes to alluvial plots
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Rmd	416e107	jens-daniel-mueller	2022-01-20	added delta dcant map
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Rmd	81a40d5	jens-daniel-mueller	2022-01-20	updated alluvial plots
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Rmd	2ae5966	jens-daniel-mueller	2022-01-20	updated alluvial plots
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Rmd	4901b0f	jens-daniel-mueller	2022-01-20	updated alluvial plots
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Rmd	cdbd92c	jens-daniel-mueller	2022-01-20	created alluvial plots
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Rmd	0fb2ae5	jens-daniel-mueller	2022-01-19	printed column inv from AIP standard runs
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Rmd	86b711c	jens-daniel-mueller	2022-01-18	plot hemisphere budgets and publication results

---

1 Libraries

2 Read files

2.1 Paths and Versions

version_id_pattern <- "1"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_x <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "o"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_o <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "d"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_d <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "g"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_g <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "n"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_n <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "c"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_c <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)

version_id_pattern <- "e"

# identify required version IDs

Version_IDs_1 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_1", version_id_pattern))

Version_IDs_2 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_2", version_id_pattern))

Version_IDs_3 <- list.files(path = "/nfs/kryo/work/jenmueller/emlr_cant/observations",
                            pattern = paste0("v_3", version_id_pattern))

Version_IDs_e <- c(Version_IDs_1, Version_IDs_2, Version_IDs_3)
rm(Version_IDs_1, Version_IDs_2, Version_IDs_3)

Version_IDs_ensemble <- c(Version_IDs_x, Version_IDs_o, Version_IDs_d, Version_IDs_g, Version_IDs_n, Version_IDs_c, Version_IDs_e)

rm(Version_IDs_x, Version_IDs_o, Version_IDs_g, Version_IDs_n, Version_IDs_c, Version_IDs_e)

# subset Standard case
Version_IDs <- Version_IDs_ensemble[str_detect(Version_IDs_ensemble, "104")]

2.2 Parameters

for (i_Version_IDs in Version_IDs_ensemble) {
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  params_local <-
    read_rds(paste(path_version_data,
                   "params_local.rds",
                   sep = ""))
  
  params_local <- bind_cols(
    Version_ID = i_Version_IDs,
    tref1 = params_local$tref1,
    tref2 = params_local$tref2,
    MLR_basins = params_local$MLR_basins
  )
  
  tref <- read_csv(paste(path_version_data,
                         "tref.csv",
                         sep = ""))
  
  params_local <- params_local %>%
    mutate(
      median_year_1 = sort(tref$median_year)[1],
      median_year_2 = sort(tref$median_year)[2],
      duration = median_year_2 - median_year_1,
      period = paste(median_year_1, "-", median_year_2)
    )
  
  if (exists("params_local_all_ensemble")) {
    params_local_all_ensemble <- bind_rows(params_local_all_ensemble, params_local)
  }
  
  if (!exists("params_local_all_ensemble")) {
    params_local_all_ensemble <- params_local
  }
  
  
}

rm(params_local,
   tref)


params_local_all_ensemble <- params_local_all_ensemble %>%
  select(Version_ID, period, MLR_basins, tref1, tref2)

params_local_all_ensemble <-
  params_local_all_ensemble %>%
  mutate(
    Version_ID_group = str_sub(Version_ID, 4, 4),
    Version_ID_group = case_when(
      Version_ID_group == "1" ~ "Bulk adjustment",
      Version_ID_group == "c" ~ "Cruise adjustment",
      Version_ID_group == "d" ~ "No adjustment",
      Version_ID_group == "g" ~ "No gap filling",
      Version_ID_group == "o" ~ "Reoccupation filter",
      Version_ID_group == "e" ~ "Surface eMLR(C*)",
      Version_ID_group == "n" ~ "C*(N)",
      TRUE ~ Version_ID_group
    )
  )

params_local_all_ensemble <-
  params_local_all_ensemble %>%
  mutate(
    MLR_basins = case_when(
      MLR_basins == "AIP" ~ "3",
      MLR_basins == "SO_AIP" ~ "3+SO",
      MLR_basins == "SO_5" ~ "5+SO",
      TRUE ~ MLR_basins
    )
  )

params_local_all <- params_local_all_ensemble %>% 
  filter(Version_ID %in% Version_IDs)

path_version_data     <-
  paste(path_observations,
        Version_IDs[1],
        "/data/",
        sep = "")

params_local <-
  read_rds(paste(path_version_data,
                 "params_local.rds",
                 sep = ""))

2.3 Budgets

for (i_Version_IDs in Version_IDs_ensemble) {
  # i_Version_IDs <- Version_IDs[1]
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  dcant_budget_global <-
    read_csv(paste(path_version_data,
                   "dcant_budget_global.csv",
                   sep = ""))
  
  dcant_budget_global_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_global_mod_truth.csv",
      sep = ""
    ))
  
  dcant_budget_global <- bind_rows(dcant_budget_global,
                                   dcant_budget_global_mod_truth)
  
  dcant_budget_global <- dcant_budget_global %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("dcant_budget_global_all")) {
    dcant_budget_global_all <-
      bind_rows(dcant_budget_global_all, dcant_budget_global)
  }
  
  if (!exists("dcant_budget_global_all")) {
    dcant_budget_global_all <- dcant_budget_global
  }
  
}

rm(dcant_budget_global,
   dcant_budget_global_mod_truth)

for (i_Version_IDs in Version_IDs_ensemble) {
  # i_Version_IDs <- Version_IDs[1]
  
  # print(i_Version_IDs)
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  dcant_budget_basin_MLR <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_MLR.csv",
                   sep = ""))
  
  dcant_budget_basin_MLR_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_basin_MLR_mod_truth.csv",
      sep = ""
    ))
  
    
  dcant_budget_basin_MLR <- bind_rows(dcant_budget_basin_MLR,
                                      dcant_budget_basin_MLR_mod_truth)
  
  dcant_budget_basin_MLR <- dcant_budget_basin_MLR %>%
    mutate(Version_ID = i_Version_IDs)
  

  if (exists("dcant_budget_basin_MLR_all")) {
    dcant_budget_basin_MLR_all <-
      bind_rows(dcant_budget_basin_MLR_all, dcant_budget_basin_MLR)
  }
  
  if (!exists("dcant_budget_basin_MLR_all")) {
    dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR
  }
  
  
  dcant_budget_basin_MLR_bias_decomposition <-
    read_csv(
      paste(
        path_version_data,
        "dcant_budget_basin_MLR_bias_decomposition.csv",
        sep = ""
      )
    )
  
  
  dcant_budget_basin_MLR_bias_decomposition <-
    dcant_budget_basin_MLR_bias_decomposition %>%
    mutate(Version_ID = i_Version_IDs)
  
  
  if (exists("dcant_budget_basin_MLR_bias_all_decomposition")) {
    dcant_budget_basin_MLR_bias_all_decomposition <-
      bind_rows(
        dcant_budget_basin_MLR_bias_all_decomposition,
        dcant_budget_basin_MLR_bias_decomposition
      )
  }
  
  if (!exists("dcant_budget_basin_MLR_bias_all_decomposition")) {
    dcant_budget_basin_MLR_bias_all_decomposition <-
      dcant_budget_basin_MLR_bias_decomposition
  }

}

rm(
  dcant_budget_basin_MLR,
  dcant_budget_basin_MLR_mod_truth,
  dcant_budget_basin_MLR_bias_decomposition
)

dcant_budget_global_all <- dcant_budget_global_all %>%
  filter(estimate == "dcant", 
         method == "total") %>% 
  select(-c(estimate, method)) %>% 
  rename(dcant = value)

dcant_budget_global_all <- dcant_budget_global_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
  filter(estimate == "dcant", 
         method == "total") %>% 
  select(-c(estimate, method)) %>% 
  rename(dcant = value)

dcant_budget_basin_MLR_all_1000 <- dcant_budget_basin_MLR_all %>%
  filter(inv_depth == 1000)

dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_basin_MLR_bias_all_decomposition <- 
  dcant_budget_basin_MLR_bias_all_decomposition %>%
  filter(inv_depth == params_global$inventory_depth_standard)

2.4 Column inventories

for (i_Version_IDs in Version_IDs_ensemble) {
  # i_Version_IDs <- Version_IDs[1]
  
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  dcant_inv <-
    read_csv(paste(path_version_data,
                   "dcant_inv.csv",
                   sep = ""))
  
  dcant_inv_mod_truth <-
    read_csv(paste(path_version_data,
                   "dcant_inv_mod_truth.csv",
                   sep = "")) %>%
    filter(method == "total") %>%
    select(-method)
  
  dcant_inv_bias <-
    read_csv(paste(path_version_data,
                   "dcant_inv_bias.csv",
                   sep = "")) %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_inv <- bind_rows(dcant_inv,
                         dcant_inv_mod_truth) %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_lat_grid <-
    read_csv(paste(path_version_data,
                   "dcant_budget_lat_grid.csv",
                   sep = "")) %>%
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_lon_grid <-
    read_csv(paste(path_version_data,
                   "dcant_budget_lon_grid.csv",
                   sep = "")) %>%
    mutate(Version_ID = i_Version_IDs)
  if (exists("dcant_inv_all")) {
    dcant_inv_all <- bind_rows(dcant_inv_all, dcant_inv)
  }
  
  if (!exists("dcant_inv_all")) {
    dcant_inv_all <- dcant_inv
  }
  
  if (exists("dcant_inv_bias_all")) {
    dcant_inv_bias_all <- bind_rows(dcant_inv_bias_all, dcant_inv_bias)
  }
  
  if (!exists("dcant_inv_bias_all")) {
    dcant_inv_bias_all <- dcant_inv_bias
  }
  
  if (exists("dcant_budget_lat_grid_all")) {
    dcant_budget_lat_grid_all <- bind_rows(dcant_budget_lat_grid_all, dcant_budget_lat_grid)
  }
  
  if (!exists("dcant_budget_lat_grid_all")) {
    dcant_budget_lat_grid_all <- dcant_budget_lat_grid
  }
  
  if (exists("dcant_budget_lon_grid_all")) {
    dcant_budget_lon_grid_all <- bind_rows(dcant_budget_lon_grid_all, dcant_budget_lon_grid)
  }
  
  if (!exists("dcant_budget_lon_grid_all")) {
    dcant_budget_lon_grid_all <- dcant_budget_lon_grid
  }
}

rm(dcant_inv,
   dcant_inv_bias,
   dcant_inv_mod_truth,
   dcant_budget_lat_grid,
   dcant_budget_lon_grid)

dcant_inv_all <- dcant_inv_all %>%
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_lat_grid_all <- dcant_budget_lat_grid_all %>% 
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_lon_grid_all <- dcant_budget_lon_grid_all %>% 
  filter(inv_depth == params_global$inventory_depth_standard)

dcant_budget_lat_grid_all <- dcant_budget_lat_grid_all %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>%
  filter(period != "1994 - 2014",
         method == "total")

dcant_budget_lon_grid_all <- dcant_budget_lon_grid_all %>%
  pivot_wider(names_from = estimate,
              values_from = value) %>%
  filter(period != "1994 - 2014",
         method == "total")

dcant_inv_all %>% 
  filter(Version_ID == "v_1103",
         data_source == "obs") %>% 
  select(lon, lat, dcant) %>% 
  write_csv(here::here("output/dcant_column_inventor_example.csv"))

2.5 Sections / profiles

for (i_Version_IDs in Version_IDs_ensemble) {

  path_version_data     <-
  paste(path_observations,
        i_Version_IDs,
        "/data/",
        sep = "")
  
  # load and join data files
  
  dcant_zonal <-
    read_csv(paste(path_version_data,
                   "dcant_zonal.csv",
                   sep = ""))
  
  dcant_zonal_mod_truth <-
    read_csv(paste(path_version_data,
                   "dcant_zonal_mod_truth.csv",
                   sep = ""))
  
  dcant_zonal <- bind_rows(dcant_zonal,
                           dcant_zonal_mod_truth)
  
  dcant_profile <-
    read_csv(paste(path_version_data,
                   "dcant_profile.csv",
                   sep = ""))
  
  dcant_profile_mod_truth <-
    read_csv(paste(path_version_data,
                   "dcant_profile_mod_truth.csv",
                   sep = ""))
  
  dcant_profile <- bind_rows(dcant_profile,
                             dcant_profile_mod_truth)
  
  
  dcant_profile_basin_MLR <-
    read_csv(paste(path_version_data,
                   "dcant_profile_basin_MLR.csv",
                   sep = ""))
  
  
  dcant_profile_basin_MLR_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_profile_basin_MLR_mod_truth.csv",
      sep = ""
    ))

  dcant_profile_basin_MLR <- bind_rows(dcant_profile_basin_MLR,
                                       dcant_profile_basin_MLR_mod_truth)
  
  
  dcant_budget_basin_AIP_layer <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_AIP_layer.csv",
                   sep = ""))

  dcant_budget_basin_MLR_layer <-
    read_csv(paste(path_version_data,
                   "dcant_budget_basin_MLR_layer.csv",
                   
                   sep = ""))
  
  dcant_budget_basin_MLR_layer_mod_truth <-
    read_csv(paste(
      path_version_data,
      "dcant_budget_basin_MLR_layer_mod_truth.csv",
      sep = ""
    ))
  
  dcant_budget_basin_MLR_layer <-
    bind_rows(dcant_budget_basin_MLR_layer,
              dcant_budget_basin_MLR_layer_mod_truth)
  
  dcant_zonal_bias <-
    read_csv(paste(path_version_data,
                   "dcant_zonal_bias.csv",
                   sep = ""))
  

  dcant_zonal <- dcant_zonal %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_profile <- dcant_profile %>% 
    mutate(Version_ID = i_Version_IDs)

  dcant_profile_basin_MLR <- dcant_profile_basin_MLR %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_basin_AIP_layer <- dcant_budget_basin_AIP_layer %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_budget_basin_MLR_layer <- dcant_budget_basin_MLR_layer %>% 
    mutate(Version_ID = i_Version_IDs)
  
  dcant_zonal_bias <- dcant_zonal_bias %>% 
    mutate(Version_ID = i_Version_IDs)

  
  if (exists("dcant_zonal_all")) {
    dcant_zonal_all <- bind_rows(dcant_zonal_all, dcant_zonal)
  }
  
  if (!exists("dcant_zonal_all")) {
    dcant_zonal_all <- dcant_zonal
  }

  if (exists("dcant_profile_all")) {
    dcant_profile_all <- bind_rows(dcant_profile_all, dcant_profile)
  }
  
  if (!exists("dcant_profile_all")) {
    dcant_profile_all <- dcant_profile
  }

  if (exists("dcant_profile_basin_MLR_all")) {
    dcant_profile_basin_MLR_all <- bind_rows(dcant_profile_basin_MLR_all, dcant_profile_basin_MLR)
  }
  
  if (!exists("dcant_profile_basin_MLR_all")) {
    dcant_profile_basin_MLR_all <- dcant_profile_basin_MLR
  }

  if (exists("dcant_budget_basin_AIP_layer_all")) {
    dcant_budget_basin_AIP_layer_all <-
      bind_rows(dcant_budget_basin_AIP_layer_all,
                dcant_budget_basin_AIP_layer)
  }
  
  if (!exists("dcant_budget_basin_AIP_layer_all")) {
    dcant_budget_basin_AIP_layer_all <- dcant_budget_basin_AIP_layer
  }

  if (exists("dcant_budget_basin_MLR_layer_all")) {
    dcant_budget_basin_MLR_layer_all <-
      bind_rows(dcant_budget_basin_MLR_layer_all,
                dcant_budget_basin_MLR_layer)
  }
  
  if (!exists("dcant_budget_basin_MLR_layer_all")) {
    dcant_budget_basin_MLR_layer_all <- dcant_budget_basin_MLR_layer
  }

  if (exists("dcant_zonal_bias_all")) {
    dcant_zonal_bias_all <- bind_rows(dcant_zonal_bias_all, dcant_zonal_bias)
  }
  
  if (!exists("dcant_zonal_bias_all")) {
    dcant_zonal_bias_all <- dcant_zonal_bias
  }

}

rm(dcant_zonal, dcant_zonal_bias, dcant_zonal_mod_truth,
   dcant_budget_basin_AIP_layer, dcant_budget_basin_MLR_layer,
   dcant_profile, dcant_profile_basin_MLR)

2.6 Observations coverage

for (i_Version_IDs in Version_IDs) {
  path_version_data     <-
    paste(path_observations,
          i_Version_IDs,
          "/data/",
          sep = "")
  
  # load and join data files
  
  GLODAP_grid_era  <-
    read_csv(paste(
      path_version_data,
      "GLODAPv2.2020_clean_obs_grid_era.csv",
      sep = ""
    ))

  
  GLODAP_grid_era <- GLODAP_grid_era %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("GLODAP_grid_era_all")) {
    GLODAP_grid_era_all <-
      bind_rows(GLODAP_grid_era_all, GLODAP_grid_era)
  }
  
  if (!exists("GLODAP_grid_era_all")) {
    GLODAP_grid_era_all <- GLODAP_grid_era
  }
  
  
  
  GLODAP <- read_csv(paste(
    path_version_data,
    "GLODAPv2.2020_clean.csv",
    sep = ""
  ))
  
  GLODAP <- GLODAP %>%
    mutate(Version_ID = i_Version_IDs)
  
  if (exists("GLODAP_all")) {
    GLODAP_all <-
      bind_rows(GLODAP_all, GLODAP)
  }
  
  if (!exists("GLODAP_all")) {
    GLODAP_all <- GLODAP
  }
  
}

rm(GLODAP_grid_era, GLODAP)


GLODAP_grid_era_all <- full_join(GLODAP_grid_era_all,
                                 params_local_all)

GLODAP_all <- full_join(GLODAP_all,
                        params_local_all)


GLODAP_expocodes <-
  read_tsv(
    paste(
      "/nfs/kryo/work/updata/glodapv2_2021/",
      "EXPOCODES.txt",
      sep = ""
    ),
    col_names = c("cruise", "cruise_expocode")
  )

GLODAP_all <-
  left_join(GLODAP_all, GLODAP_expocodes)

2.7 Atm CO2

co2_atm <-
  read_csv(paste(path_preprocessing,
                 "co2_atm.csv",
                 sep = ""))

co2_atm_reccap2 <-
  read_csv(paste(path_preprocessing,
                  "co2_atm_reccap2.csv",
                  sep = ""))

2.8 Sabine total Cant

tcant_inv <-
  read_csv(paste(path_preprocessing,
                  "S04_tcant_inv.csv", sep = ""))

tcant_inv <- tcant_inv %>% 
  filter(inv_depth == 3000) %>% 
  rename(dcant = tcant,
         dcant_pos = tcant_pos) %>% 
  mutate(tref1 = 1800,
         tref2 = 1994)

2.9 GCB carbon sink

Ocean_Sink <-
  read_csv(paste(path_preprocessing,
                  "Ocean_Sink.csv",
                  sep = ""))

Global_Carbon_Budget <-
  read_csv(paste(path_preprocessing,
                  "GCB_Global_Carbon_Budget.csv",
                  sep = ""))

2.10 Periods

two_decades <- unique(params_local_all_ensemble$period)[1:2]

3 Define labels

dcant_pgc_label <- expression(Delta * C["ant"]~(PgC))
dcant_bias_pgc_label <- expression(Bias ~ Delta * C["ant"]~(PgC))
delta_dcant_bias_pgc_label <- expression(Bias ~ Delta * Delta * C["ant"]~(PgC))
dcant_pgc_scaled_label <- expression(beta~(PgC~µatm^{-1}))

dcant_umol_label <- expression(Delta * C[ant]~(mu * mol ~ kg ^ {-1}))
ddcant_umol_label <- expression(Delta * Delta * C[ant]~(mu * mol ~ kg ^ {-1}))

dcant_layer_budget_label <- 
  expression(Delta ~ C[ant] ~ "budget per 500m depth layer (PgC)")

dcant_CI_label <- expression(Delta * C[ant]~(mol ~ m ^ {-2}))
beta_CI_label <- expression(beta~(mol ~ m ^ {-2} ~ µatm ^ {-1}))

4 Coverage maps

cruises_phosphate_gap_fill <-
  c("33MW19930704",
    "33RO20030604",
    "33RO20050111",
    "33RO19980123")

cruises_talk_gap_fill <-
  c("06AQ19980328")

cruises_talk_calc <-
  c("06MT19900123",
    "316N19920502",
    "316N19921006")

GLODAP_all_coverage <- GLODAP_all %>% 
  distinct(lat, lon, era, cruise_expocode) %>% 
  mutate(gap_filling = case_when(
    cruise_expocode %in% cruises_phosphate_gap_fill ~ "PO4 w CANYON-B",
    cruise_expocode %in% cruises_talk_gap_fill ~ "TA w CANYON-B",
    cruise_expocode %in% cruises_talk_calc ~ "TA calculated",
    cruise_expocode %in% "31DS19940126" ~ "NO3 qc flag",
    TRUE ~ "None"
  )) %>% 
  distinct(lat, lon, era, gap_filling)

coverage_map <- map +
  geom_tile(data = GLODAP_all_coverage %>% filter(gap_filling == "None"),
            aes(lon, lat),
            fill = "#BBBBBB",
            col = "#BBBBBB") +
  geom_tile(data = GLODAP_all_coverage %>% filter(gap_filling != "None"),
            aes(lon, lat,
            fill = gap_filling, col = gap_filling)) +
  facet_wrap( ~ era, ncol = 2) +
  scale_color_bright(name = "Gap-filling") +
  scale_fill_bright(name = "Gap-filling") +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank(),
        legend.position = c(0.7,0.3))

coverage_map

Past versions of coverage_maps-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
9ce772d	jens-daniel-mueller	2022-07-19
16dc3af	jens-daniel-mueller	2022-06-27
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04

ggsave(plot = coverage_map,
       path = here::here("output/publication"),
       filename = "FigS_observations_coverage_map.png",
       height = 4,
       width = 8)

rm(coverage_map, 
   GLODAP_all, GLODAP_all_coverage, GLODAP_expocodes,
   GLODAP_grid_era_all)


rm(cruises_phosphate_gap_fill,
   cruises_talk_gap_fill,
   cruises_talk_calc)

5 Atm CO2

co2_atm_roll <- co2_atm %>% 
  mutate(pCO2_roll = zoo::rollmean(pCO2, 10, fill = NA))

p_pco2_atm <- co2_atm_roll %>% 
  ggplot() +
  geom_point(aes(year, pCO2, col="annual")) +
  geom_path(aes(year, pCO2, col="annual")) +
  geom_point(aes(year, pCO2_roll, col="10yr roll")) +
  geom_path(aes(year, pCO2_roll, col="10yr roll"))

co2_atm_roll <- co2_atm_roll %>% 
  mutate(delta_pCO2_roll = pCO2_roll - lag(pCO2_roll,10),
         delta_pCO2 = pCO2 - lag(pCO2,10))

p_delta_pCO2_atm <- co2_atm_roll %>% 
  ggplot() +
  geom_point(aes(year, delta_pCO2, col="annual")) +
  geom_path(aes(year, delta_pCO2, col="annual")) +
  geom_point(aes(year, delta_pCO2_roll, col="10yr roll")) +
  geom_path(aes(year, delta_pCO2_roll, col="10yr roll"))

p_pco2_atm / p_delta_pCO2_atm

Past versions of atm_CO2-1.png

Version	Author	Date
0160c40	jens-daniel-mueller	2022-07-16
022fd60	jens-daniel-mueller	2022-07-14

rm(p_pco2_atm, p_delta_pCO2_atm)

delta_pCO2_atm <-
  left_join(
    params_local_all %>%
      distinct(period, tref1, tref2),
    co2_atm_roll %>%
      select(
        tref1 = year,
        pCO2_1 = pCO2,
        pCO2_roll_1 = pCO2_roll
      )
  )

delta_pCO2_atm <-
  left_join(
    delta_pCO2_atm,
    co2_atm_roll %>%
      select(
        tref2 = year,
        pCO2_2 = pCO2,
        pCO2_roll_2 = pCO2_roll
      )
  )

delta_pCO2_atm <- delta_pCO2_atm %>% 
  mutate(delta_pCO2 = pCO2_2 - pCO2_1,
         delta_pCO2_roll = pCO2_roll_2 - pCO2_roll_1) %>% 
  select(period, delta_pCO2, delta_pCO2_roll)

delta_pCO2_atm <- delta_pCO2_atm %>%
  select(period, delta_pCO2)

delta_pCO2_atm <- 
  bind_rows(
    delta_pCO2_atm,
    tibble(period = "1800 - 1994",
           delta_pCO2 = co2_atm %>% filter(year==1994) %>% pull(pCO2) - 280)
  )

rm(co2_atm_roll)

6 Globale scaling

• Accumulation beneath 3000m: +2%
• unmapped regions south of 65N: +1.5%
• Nordic Seas: +1%
• Arctic Ocean: +2%

dcant_scaling <- 1.02*1.015*1.01*1.02

7 Inventory maps

7.1 Absoulte values

7.1.1 dcant - absolute

# dcant_inv_all <- dcant_inv_all %>%
#   filter(data_source %in% c("obs"))


dcant_inv_all <- bind_rows(
  dcant_inv_all,
  tcant_inv %>% mutate(period = paste(tref1, tref2, sep = " - "),
                       data_source = "obs")
)


dcant_inv_all <- left_join(dcant_inv_all,
                           delta_pCO2_atm)


dcant_inv_all <- dcant_inv_all %>% 
  mutate(beta = dcant / delta_pCO2,
         beta_pos = dcant_pos / delta_pCO2) %>% 
  select(-starts_with("pCO2"))

dcant_inv_all_ensemble <- dcant_inv_all

dcant_inv_all <- dcant_inv_all %>% 
  filter(Version_ID %in% Version_IDs | is.na(Version_ID))

p_CI_absolute <- dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source %in% c("obs")) %>%
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_wrap(~ period, ncol = 1) +
  theme(axis.text.x = element_blank()) 

p_CI_absolute

Past versions of invetory_maps_absolute-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
09b0780	jens-daniel-mueller	2022-05-24
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
08607eb	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
b599680	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
c3a6238	jens-daniel-mueller	2022-03-08
094bfa0	jens-daniel-mueller	2022-02-18
d2191ad	jens-daniel-mueller	2022-02-04
32e9682	jens-daniel-mueller	2022-02-02
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

dcant_inv_stat_mean <- 
full_join(basinmask,
          dcant_inv_all) %>% 
  mutate(surf_area = earth_surf(lat, lon)) %>% 
  group_by(basin, period, data_source) %>% 
  summarise(dcant = weighted.mean(dcant, surf_area)) %>% 
  ungroup() %>% 
  filter(!is.na(dcant))
  
dcant_inv_stat_ensemble <- 
full_join(basinmask,
          dcant_inv_all_ensemble) %>% 
  mutate(surf_area = earth_surf(lat, lon)) %>% 
  group_by(basin, data_source, period, MLR_basins, Version_ID_group) %>% 
  summarise(dcant = weighted.mean(dcant, surf_area)) %>% 
  ungroup() %>% 
  filter(!is.na(dcant)) %>% 
  group_by(basin, data_source, period) %>% 
  summarise(dcant_sd = sd(dcant)*2) %>% 
  ungroup()

dcant_inv_stat <- 
full_join(dcant_inv_stat_mean,
          dcant_inv_stat_ensemble)  

rm(dcant_inv_stat_mean,
          dcant_inv_stat_ensemble)  

dcant_inv_stat %>%
  filter(period %in% two_decades) %>%
  group_by(data_source, basin) %>%
  mutate(delta_dcant = dcant - lag(dcant),
         RSS = sqrt(dcant_sd ^ 2 + lag(dcant_sd ^ 2))) %>%
  ungroup() %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	period	data_source	dcant	dcant_sd	delta_dcant	RSS
N. Pacific	1994 - 2004	mod	3.527437	0.5328718	NA	NA
N. Pacific	1994 - 2004	mod_truth	2.915800	0.0000000	NA	NA
N. Pacific	1994 - 2004	obs	2.347195	2.3109782	NA	NA
N. Pacific	2004 - 2014	mod	3.608933	0.6769466	0.0814953	0.8615156
N. Pacific	2004 - 2014	mod_truth	3.352012	0.0000000	0.4362120	0.0000000
N. Pacific	2004 - 2014	obs	4.038812	3.2083219	1.6916170	3.9539790
S. Pacific	1994 - 2004	mod	4.543124	1.3206295	NA	NA
S. Pacific	1994 - 2004	mod_truth	4.931744	0.0000000	NA	NA
S. Pacific	1994 - 2004	obs	7.278204	2.2631886	NA	NA
S. Pacific	2004 - 2014	mod	5.896297	0.9408415	1.3531731	1.6214947
S. Pacific	2004 - 2014	mod_truth	6.060215	0.0000000	1.1284704	0.0000000
S. Pacific	2004 - 2014	obs	6.600731	1.7210649	-0.6774732	2.8432529
N. Atlantic	1994 - 2004	mod	4.834997	1.0037616	NA	NA
N. Atlantic	1994 - 2004	mod_truth	4.057536	0.0000000	NA	NA
N. Atlantic	1994 - 2004	obs	9.970307	1.0432559	NA	NA
N. Atlantic	2004 - 2014	mod	4.282947	0.9804729	-0.5520499	1.4031624
N. Atlantic	2004 - 2014	mod_truth	4.878549	0.0000000	0.8210126	0.0000000
N. Atlantic	2004 - 2014	obs	7.652439	1.6768584	-2.3178686	1.9749018
S. Atlantic	1994 - 2004	mod	5.579086	1.2343116	NA	NA
S. Atlantic	1994 - 2004	mod_truth	4.706655	0.0000000	NA	NA
S. Atlantic	1994 - 2004	obs	5.449012	2.3124413	NA	NA
S. Atlantic	2004 - 2014	mod	5.767762	0.9977609	0.1886764	1.5871521
S. Atlantic	2004 - 2014	mod_truth	5.251166	0.0000000	0.5445113	0.0000000
S. Atlantic	2004 - 2014	obs	9.647058	2.1252317	4.1980461	3.1406997
Indian	1994 - 2004	mod	5.875285	1.9889221	NA	NA
Indian	1994 - 2004	mod_truth	5.570926	0.0000000	NA	NA
Indian	1994 - 2004	obs	8.069098	4.0341866	NA	NA
Indian	2004 - 2014	mod	7.638433	1.2869140	1.7631482	2.3689573
Indian	2004 - 2014	mod_truth	6.501337	0.0000000	0.9304108	0.0000000
Indian	2004 - 2014	obs	6.547764	1.7211668	-1.5213341	4.3860092

7.1.2 dcant - absolute delta

p_CI_delta <-
  dcant_inv_all %>%
  filter(data_source %in% c("obs"),
         period %in% two_decades) %>%
  select(data_source, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(dcant_offset = `2004 - 2014` - `1994 - 2004`,
         period = "(2004 - 2014) - (1994 - 2004)") %>%
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_map_cant_inv(
      df = .x,
      var = "dcant_offset",
      title_text = NULL,
      subtitle_text = NULL,
      col = "bias"
    ) +
      facet_wrap(~ period)
  )

p_CI_absolute / p_CI_delta + 
  plot_annotation(tag_levels = 'A')

Past versions of invetory_maps_absolute_delta-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
99a82db	jens-daniel-mueller	2022-07-25
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
efa414b	jens-daniel-mueller	2022-07-16
f1d7f80	jens-daniel-mueller	2022-07-13
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
748aa43	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
b599680	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
c3a6238	jens-daniel-mueller	2022-03-08
094bfa0	jens-daniel-mueller	2022-02-18
32e9682	jens-daniel-mueller	2022-02-02
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_column_inventory.png",
       height = 10,
       width = 8)

rm(p_CI_absolute, p_CI_delta)

7.1.3 Bias

p_CI_absolute_mod <- dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source != "obs") %>%
  mutate(data_source = case_when(
    data_source == "mod" ~ "Reconstruction",
    data_source == "mod_truth" ~ "Model truth"
  )) %>%  
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_grid(data_source ~ period) +
  theme(axis.text.x = element_blank()) 

p_CI_absolute_mod

Past versions of invetory_maps_absolute_mod-1.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10

p_CI_bias <-
  dcant_inv_all %>%
  filter(period %in% two_decades,
         data_source != "obs") %>%
  select(data_source, lon, lat, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = data_source,
              values_from = dcant) %>%
  mutate(dcant_offset = mod - mod_truth,
         data_source = "Reconstruction bias") %>%
  p_map_cant_inv(
    var = "dcant_offset",
    title_text = NULL,
    subtitle_text = NULL,
    col = "bias"
  ) +
  facet_grid(data_source ~ period) +
  theme(strip.text.x = element_blank(),
        strip.background.x = element_blank())

p_CI_absolute_mod / p_CI_bias + 
  plot_annotation(tag_levels = 'A')

Past versions of invetory_maps_absolute_mod_bias-1.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_column_inventory_synthetic_data.png",
       height = 10,
       width = 11)

rm(p_CI_absolute_mod, p_CI_bias)

7.1.4 Ensemble SD

dcant_inv_all_ensemble_mean <-
  dcant_inv_all_ensemble %>%
  filter(period != "1800 - 1994",
         data_source == "obs") %>%
  group_by(data_source, period, lon, lat) %>%
  summarise(dcant_sd = sd(dcant)*2) %>%
  ungroup()


dcant_inv_all_ensemble_mean %>%
  p_map_cant_inv(var = "dcant_sd",
                 breaks = c(seq(0, 8, 1), Inf),
                 title_text = NULL) +
  facet_grid(period ~ .) +
  theme(axis.text = element_blank(),
        axis.ticks = element_blank())

Past versions of column_inventory_standard_deviation-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
0160c40	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
f09080e	jens-daniel-mueller	2022-06-28
16dc3af	jens-daniel-mueller	2022-06-27

# ggsave(path = here::here("output/publication"),
#        filename = "FigS_dcant_column_inventory_ensemble_standard_deviation.png",
#        height = 6,
#        width = 6)

7.1.5 Unadjusted data

dcant_inv_all_unadjusted <-
  dcant_inv_all_ensemble %>%
  filter(period %in% two_decades,
         data_source == "obs",
         MLR_basins == "3",
         Version_ID %in% Version_IDs |
           Version_ID %in% Version_IDs_d)


dcant_inv_all_unadjusted %>%
  p_map_cant_inv(var = "dcant",
                 title_text = NULL) +
  facet_grid(Version_ID_group ~ period)

Past versions of column_inventory_unadjusted_data-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
fea41c1	jens-daniel-mueller	2022-07-20
9ce772d	jens-daniel-mueller	2022-07-19

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_column_inventory_unadjusted_data.png",
       height = 4,
       width = 8)

7.1.6 beta

dcant_inv_all %>%
  filter(data_source %in% c("obs")) %>% 
  p_map_cant_inv(var = "beta",
                 breaks = c(-Inf, seq(0, 1, 0.1), Inf),
                 title_text = NULL) +
  facet_grid(period ~ .)

Past versions of beta_CI_maps-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
efa414b	jens-daniel-mueller	2022-07-16

ggsave(path = here::here("output/publication"),
       filename = "FigS_beta_column_inventory.png",
       height = 8,
       width = 6)

7.2 Zonal means

dcant_inv_all <- m_grid_horizontal_coarse(dcant_inv_all)

dcant_inv_all_lat_mean <- dcant_inv_all %>% 
  group_by(basin_AIP, period, lat_grid, data_source) %>% 
  summarise(beta = mean(beta, na.rm = TRUE),
            dcant = mean(dcant, na.rm = TRUE)) %>% 
  ungroup()

dcant_inv_all_ensemble <- m_grid_horizontal_coarse(dcant_inv_all_ensemble)

dcant_inv_all_ensemble_lat_mean <- dcant_inv_all_ensemble %>% 
  group_by(basin_AIP, period, lat_grid, Version_ID, data_source) %>% 
  summarise(beta_mean = mean(beta, na.rm = TRUE),
            dcant_mean = mean(dcant, na.rm = TRUE)) %>% 
  ungroup()

dcant_inv_all_ensemble_lat_mean_average <- dcant_inv_all_ensemble_lat_mean %>% 
  group_by(basin_AIP, period, lat_grid, data_source) %>% 
  summarise(beta_sd = sd(beta_mean, na.rm = TRUE),
            beta_mean = mean(beta_mean, na.rm = TRUE),
            dcant_sd = sd(dcant_mean, na.rm = TRUE),
            dcant_mean = mean(dcant_mean, na.rm = TRUE)) %>% 
  ungroup()

dcant_inv_all_lat_mean <- full_join(dcant_inv_all_lat_mean,
                                    dcant_inv_all_ensemble_lat_mean_average)

7.2.1 dcant

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  ggplot(aes(lat_grid, dcant, fill = period)) +
  geom_hline(yintercept = c(0,10)) +
  geom_vline(xintercept = c(-10,10)) +
  geom_vline(xintercept = c(-40,-30,30,40)) +
  geom_ribbon(aes(ymin = dcant - dcant_sd,
                  ymax = dcant + dcant_sd),
              alpha = 0.3) +
  geom_path(aes(col = period)) +
  geom_point(shape = 21) +
  scale_color_brewer(palette = "Dark2", name = "Mean ± SD") +
  scale_fill_brewer(palette = "Dark2", name = "Mean ± SD") +
  coord_flip() +
  facet_grid(data_source ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = dcant_CI_label) +
  scale_x_continuous(breaks = seq(-75,75, 15))

Past versions of zonal_mean_storage_dcant-1.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
ea46812	jens-daniel-mueller	2022-07-13

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  mutate(lat_grid = cut(lat_grid, c(-40,-30,-10,10,30,40))) %>% 
  group_by(lat_grid, data_source) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 18 × 3
   lat_grid  data_source dcant_mean
   <fct>     <chr>            <dbl>
 1 (-40,-30] mod              11.7 
 2 (-40,-30] mod_truth        10.9 
 3 (-40,-30] obs              14.4 
 4 (-30,-10] mod               8.74
 5 (-30,-10] mod_truth         7.73
 6 (-30,-10] obs              11.6 
 7 (-10,10]  mod               4.65
 8 (-10,10]  mod_truth         4.09
 9 (-10,10]  obs               7.45
10 (10,30]   mod               5.40
11 (10,30]   mod_truth         5.03
12 (10,30]   obs               7.46
13 (30,40]   mod               6.27
14 (30,40]   mod_truth         6.46
15 (30,40]   obs              11.0 
16 <NA>      mod               5.62
17 <NA>      mod_truth         5.59
18 <NA>      obs               7.35

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  mutate(lat_grid = cut(lat_grid, c(-40,-30,-10,10,30,40))) %>% 
  group_by(lat_grid, basin_AIP, data_source) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 51 × 4
   lat_grid  basin_AIP data_source dcant_mean
   <fct>     <chr>     <chr>            <dbl>
 1 (-40,-30] Atlantic  mod              11.3 
 2 (-40,-30] Atlantic  mod_truth        10.1 
 3 (-40,-30] Atlantic  obs              15.6 
 4 (-40,-30] Indian    mod              13.4 
 5 (-40,-30] Indian    mod_truth        12.6 
 6 (-40,-30] Indian    obs              14.5 
 7 (-40,-30] Pacific   mod              10.6 
 8 (-40,-30] Pacific   mod_truth         9.97
 9 (-40,-30] Pacific   obs              13.3 
10 (-30,-10] Atlantic  mod               7.55
# … with 41 more rows

dcant_inv_all_lat_mean %>%
  filter(period != "1800 - 1994") %>% 
  mutate(lat_grid = cut(lat_grid, c(-90,0,90))) %>% 
  group_by(lat_grid, basin_AIP, period, data_source) %>% 
  summarise(dcant_mean = mean(dcant_mean, na_rm = TRUE)) %>% 
  ungroup()

# A tibble: 54 × 5
   lat_grid basin_AIP period      data_source dcant_mean
   <fct>    <chr>     <chr>       <chr>            <dbl>
 1 (-90,0]  Atlantic  1994 - 2004 mod               4.29
 2 (-90,0]  Atlantic  1994 - 2004 mod_truth         4.06
 3 (-90,0]  Atlantic  1994 - 2004 obs               7.31
 4 (-90,0]  Atlantic  1994 - 2014 mod               9.63
 5 (-90,0]  Atlantic  1994 - 2014 mod_truth         8.72
 6 (-90,0]  Atlantic  1994 - 2014 obs              15.2 
 7 (-90,0]  Atlantic  2004 - 2014 mod               5.42
 8 (-90,0]  Atlantic  2004 - 2014 mod_truth         4.66
 9 (-90,0]  Atlantic  2004 - 2014 obs               7.71
10 (-90,0]  Indian    1994 - 2004 mod               5.90
# … with 44 more rows

dcant_inv_all_ensemble_lat_mean %>% 
  ggplot(aes(lat_grid, dcant_mean, fill=period, group=Version_ID)) +
  geom_hline(yintercept = 0) +
  geom_path(aes(col=period), alpha = 0.3) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  coord_flip() +
  facet_grid(data_source ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = dcant_CI_label)

Past versions of zonal_mean_storage_dcant-2.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
0160c40	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
ea46812	jens-daniel-mueller	2022-07-13

7.2.2 beta

dcant_inv_all_lat_mean %>%
  ggplot(aes(lat_grid, beta, fill = period)) +
  geom_hline(yintercept = 0) +
  geom_ribbon(aes(ymin = beta - beta_sd,
                  ymax = beta + beta_sd),
              alpha = 0.3) +
  geom_path(aes(col = period)) +
  geom_point(shape = 21) +
  scale_color_brewer(palette = "Dark2", name = "Mean ± SD") +
  scale_fill_brewer(palette = "Dark2", name = "Mean ± SD") +
  coord_flip() +
  facet_grid(data_source ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = beta_CI_label) +
  scale_x_continuous(breaks = seq(-75,75, 15))

Past versions of zonal_mean_storage_beta-1.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
ea46812	jens-daniel-mueller	2022-07-13

dcant_inv_all_ensemble_lat_mean %>% 
  ggplot(aes(lat_grid, beta_mean, fill=period, group=Version_ID)) +
  geom_hline(yintercept = 0) +
  geom_path(aes(col=period), alpha = 0.3) +
  scale_color_brewer(palette = "Dark2") +
  scale_fill_brewer(palette = "Dark2") +
  coord_flip() +
  facet_grid(data_source ~ basin_AIP) +
  labs(x = "Latitude (°N)",
       y = beta_CI_label)

Past versions of zonal_mean_storage_beta-2.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
0160c40	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
ea46812	jens-daniel-mueller	2022-07-13

8 Zonal sections

8.1 dcant - absolut

p_dcant_zonal_absolute <-
  dcant_zonal_all %>%
  filter(
    data_source == "obs",
    period %in% two_decades,
    depth <= params_global$inventory_depth_standard
  ) %>%
  p_section_zonal_continous_depth(var = "dcant",
                                  plot_slabs = "n",
                                  title_text = NULL) +
  geom_contour(aes(lat, depth, z = dcant),
               breaks = 5,
               col = "white") +
  # geom_text_contour(
  #   aes(lat, depth, z = dcant),
  #   breaks = 5,
  #   stroke = 0.2,
  #   stroke.colour = "white",
  #   rotate = FALSE,
  #   label.placer = label_placer_n(n = 1)
  # ) +
  facet_grid(period ~ basin_AIP) +
  theme(
    axis.text.x = element_blank(),
    axis.title.x = element_blank(),
    legend.position = "top"
  ) +
  guides(fill = guide_colorsteps(
    barheight = unit(0.5, "cm"),
    barwidth = unit(7, "cm"),
    show.limits = FALSE
  ))

p_dcant_zonal_absolute

Past versions of zonal_sections-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
e2adac6	jens-daniel-mueller	2022-07-22
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
ea46812	jens-daniel-mueller	2022-07-13
16dc3af	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
09b0780	jens-daniel-mueller	2022-05-24
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
094bfa0	jens-daniel-mueller	2022-02-18
d2191ad	jens-daniel-mueller	2022-02-04
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
e572075	jens-daniel-mueller	2022-01-21

dcant_zonal_all %>%
  filter(
    data_source == "obs",
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard
  ) %>%
  group_split(basin_AIP) %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "dcant",
      breaks = c(-Inf, -1, 0, 1, seq(2,16,2), Inf),
      plot_slabs = "n",
      title_text = .x$basin_AIP,
      subtitle_text = NULL
    ) +
      facet_grid(period ~ .)
  )

8.2 dcant - absolute delta

dcant_zonal_all %>%
  filter(data_source %in% c("mod", "obs"),
         period != "1994 - 2014") %>%
  select(data_source, lat, depth, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "delta_dcant",
      plot_slabs = "n",
      subtitle_text = unique(.x$data_source),
      col = "bias"
    ) +
      facet_grid(basin_AIP ~ .)
  )

[[1]]

Past versions of inventory_maps_absolute_delta-1.png

Version	Author	Date
e2adac6	jens-daniel-mueller	2022-07-22
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
16dc3af	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
094bfa0	jens-daniel-mueller	2022-02-18
d2191ad	jens-daniel-mueller	2022-02-04
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
d6b399a	jens-daniel-mueller	2022-01-21

[[2]]

Past versions of inventory_maps_absolute_delta-2.png

Version	Author	Date
e2adac6	jens-daniel-mueller	2022-07-22
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28
16dc3af	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
094bfa0	jens-daniel-mueller	2022-02-18
d2191ad	jens-daniel-mueller	2022-02-04
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
d6b399a	jens-daniel-mueller	2022-01-21

p_dcant_zonal_delta <-
  dcant_zonal_all %>%
  filter(data_source %in% c("obs"),
         period != "1994 - 2014") %>%
  select(data_source, lat, depth, basin_AIP, period, gamma_mean, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
  group_by(data_source) %>%
  group_split() %>%
  # head(1) %>%
  map(
    ~ p_section_zonal_continous_depth(
      df = .x,
      var = "delta_dcant",
      plot_slabs = "y",
      drop_slabs = 1,
      subtitle_text = NULL,
      title_text = NULL,
      col = "bias"
    ) +
      facet_grid("Dec. diff." ~ basin_AIP) +
      theme(
        strip.text.x = element_blank(),
        strip.background.x = element_blank(),
        legend.position = "bottom"
      ) +
      guides(fill = guide_colorsteps(
        barheight = unit(0.5, "cm"),
        barwidth = unit(10, "cm"),
        show.limits = FALSE
      ))
  )

p_dcant_zonal_absolute / p_dcant_zonal_delta + 
  plot_layout(heights = c(2,1)) +
  plot_annotation(tag_levels = 'A')

Past versions of inventory_maps_absolute_delta-3.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
e2adac6	jens-daniel-mueller	2022-07-22
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
d20faeb	jens-daniel-mueller	2022-07-17
0160c40	jens-daniel-mueller	2022-07-16
f1d7f80	jens-daniel-mueller	2022-07-13
ea46812	jens-daniel-mueller	2022-07-13
f09080e	jens-daniel-mueller	2022-06-28

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_zonal_mean_section.png",
       height = 6,
       width = 8)

rm(p_dcant_zonal_absolute, p_dcant_zonal_delta)

8.3 Bias

p_dcant_zonal_absolute_mod <-
  dcant_zonal_all %>%
  filter(
    data_source != "obs",
    period %in% two_decades,
    depth <= params_global$inventory_depth_standard
  ) %>%
    mutate(data_source = case_when(
    data_source == "mod" ~ "Reconstruction",
    data_source == "mod_truth" ~ "Model truth"
  )) %>%  
  p_section_zonal_continous_depth(var = "dcant",
                                  plot_slabs = "n",
                                  title_text = NULL) +
  geom_contour(aes(lat, depth, z = dcant),
               breaks = 5,
               col = "white") +
  facet_nested(data_source+period ~ basin_AIP) +
  theme(
    axis.text.x = element_blank(),
    axis.title.x = element_blank(),
    legend.position = "top"
  ) +
  guides(fill = guide_colorsteps(
    barheight = unit(0.5, "cm"),
    barwidth = unit(7, "cm"),
    show.limits = FALSE
  ))

p_dcant_zonal_absolute_mod

Past versions of zonal_sections_mod-1.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10

p_dcant_zonal_bias <-
  dcant_zonal_all %>%
  filter(data_source != "obs",
         period %in% two_decades) %>%
  select(data_source, lat, depth, basin_AIP, period, dcant) %>%
  pivot_wider(names_from = data_source,
              values_from = dcant) %>%
  mutate(delta_dcant = mod - mod_truth) %>%
  p_section_zonal_continous_depth(
    var = "delta_dcant",
    plot_slabs = "n",
    drop_slabs = 1,
    subtitle_text = NULL,
    title_text = NULL,
    col = "bias"
  ) +
  facet_grid(period ~ basin_AIP) +
  theme(
    strip.text.x = element_blank(),
    strip.background.x = element_blank(),
    legend.position = "bottom"
  ) +
  guides(fill = guide_colorsteps(
    barheight = unit(0.5, "cm"),
    barwidth = unit(10, "cm"),
    show.limits = FALSE
  ))

p_dcant_zonal_absolute_mod / p_dcant_zonal_bias + 
  plot_layout(heights = c(2,1)) +
  plot_annotation(tag_levels = 'A')

Past versions of inventory_maps_absolute_bias-1.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10

ggsave(
  path = here::here("output/publication"),
  filename = "FigS_dcant_zonal_mean_section_synthetic_data.png",
  height = 9,
  width = 8
)

rm(p_dcant_zonal_absolute_mod, 
   p_dcant_zonal_bias)

9 Concentration profiles

9.1 dcant, abs

9.1.1 Global

dcant_profile_global_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         depth <= params_global$inventory_depth_standard) %>%
  group_split(data_source) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(dcant,
                 depth)) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_ribbon(
        aes(
          xmin = dcant - dcant_sd,
          xmax = dcant + dcant_sd,
          fill = period
        ),
        alpha = 0.3
      ) +
      geom_path(aes(col = period)) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      # scale_y_reverse(breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Set1",
        name = "mean \u00B1 sd",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        name = "mean \u00B1 sd",
        direction = -1
      ) +
      labs(
        title = paste("data_source", unique(.x$data_source)),
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      facet_wrap( ~ basin, ncol = 3, dir = "v") +
      theme(legend.position = c(0.8, 0.2))
  )

[[1]]

Past versions of concentration_profiles_global-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
17cd1d1	jens-daniel-mueller	2022-07-13

[[2]]

Past versions of concentration_profiles_global-2.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
17cd1d1	jens-daniel-mueller	2022-07-13

[[3]]

Past versions of concentration_profiles_global-3.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11

mean_surface_dcant <- dcant_profile_global_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(depth <= 50) %>%
  group_by(period) %>% 
  summarise(mean_surface_dcant = mean(dcant)) %>% 
  ungroup()

full_join(dcant_profile_global_all_ensemble,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>%
  filter(dcant >= mean_surface_dcant / 2) %>%
  group_by(period, Version_ID) %>%
  summarise(max_depth = max(depth)) %>%
  ungroup() %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth))

# A tibble: 1 × 2
  sd_max_depth mean_max_depth
         <dbl>          <dbl>
1         102.           321.

9.1.2 Basin hemisphere

dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         depth <= params_global$inventory_depth_standard) %>%
  group_split(data_source) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(dcant,
                 depth)) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_ribbon(
        aes(
          xmin = dcant - dcant_sd,
          xmax = dcant + dcant_sd,
          fill = period
        ),
        alpha = 0.3
      ) +
      geom_path(aes(col = period)) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Set1",
        name = "mean \u00B1 sd",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        name = "mean \u00B1 sd",
        direction = -1
      ) +
      labs(
        title = paste("data_source", unique(.x$data_source)),
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      facet_wrap( ~ basin, ncol = 3, dir = "v") +
      theme(legend.position = c(0.8, 0.2))
  )

[[1]]

Past versions of concentration_profiles_MLR_basins-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
4673df5	jens-daniel-mueller	2022-02-02
60727e6	jens-daniel-mueller	2022-02-02
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
c6fe495	jens-daniel-mueller	2022-01-26
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21

[[2]]

Past versions of concentration_profiles_MLR_basins-2.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
748aa43	jens-daniel-mueller	2022-06-27
16dc3af	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
4673df5	jens-daniel-mueller	2022-02-02
60727e6	jens-daniel-mueller	2022-02-02
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
c6fe495	jens-daniel-mueller	2022-01-26
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21

[[3]]

Past versions of concentration_profiles_MLR_basins-3.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11

9.2 dcant, abs, ensemble

9.2.1 Global

dcant_profile_global_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source == "obs"
  ) %>%
  group_split(data_source) %>%
  map(
    ~ ggplot() +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path(
        data = .x,
        aes(
          dcant,
          depth,
          col = period,
          group = Version_ID,
          linetype = "Ensemble member",
          size = "Ensemble member"
        ), alpha = 0.5
      ) +
      geom_path(
        data = .x %>% filter(Version_ID %in% Version_IDs),
        aes(
          dcant,
          depth,
          col = period,
          group = Version_ID,
          linetype = "Standard case",
          size = "Standard case"
        )
      ) +
      scale_size_manual(values = c(0.5, 2), name="x") +
      scale_linetype_manual(values = c(2, 1), name="x") +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Set1",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        direction = -1
      ) +
      labs(
        # title = paste("data_source", unique(.x$data_source)),
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      facet_wrap( ~ basin, ncol = 3, dir = "v") +
      theme(legend.position = c(0.85, 0.3),
            legend.title = element_blank())
  )

[[1]]

Past versions of concentration_profiles_global_ensemble-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13

9.2.2 basin hemisphere

dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source == "obs"
  ) %>%
  group_split(data_source) %>%
  map(
    ~ ggplot() +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path(
        data = .x,
        aes(
          dcant,
          depth,
          col = period,
          group = Version_ID,
          linetype = "Ensemble member",
          size = "Ensemble member",
          alpha = "Ensemble member"
        )
      ) +
      geom_path(
        data = .x %>% filter(Version_ID %in% Version_IDs),
        aes(
          dcant,
          depth,
          col = period,
          group = Version_ID,
          linetype = "Standard case",
          alpha = "Standard case",
          size = "Standard case"
        )
      ) +
      geom_path(
        data = .x %>% filter(Version_ID %in% Version_IDs),
        aes(
          dcant,
          depth,
          group = Version_ID
        )
      ) +
      scale_size_manual(values = c(0.7, 1.5), name="x") +
      scale_linetype_manual(values = c(1, 1), name="x") +
      scale_alpha_manual(values = c(0.4, 1), name="x") +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Set1",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        direction = -1
      ) +
      labs(
        # title = paste("data_source", unique(.x$data_source)),
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      facet_wrap( ~ basin, ncol = 3, dir = "v") +
      theme(legend.position = "top")
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_individual-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
f1d7f80	jens-daniel-mueller	2022-07-13

mean_surface_dcant <- dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(depth <= 50) %>%
  group_by(basin, period) %>% 
  summarise(mean_surface_dcant = mean(dcant)) %>% 
  ungroup()

full_join(dcant_profile_basin_MLR_all,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(dcant >= mean_surface_dcant/2) %>%
  group_by(period, basin, Version_ID) %>% 
  summarise(max_depth = max(depth)) %>% 
  ungroup() %>% 
  group_by(basin, period) %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth)) %>% 
  ungroup()

# A tibble: 12 × 4
   basin       period      sd_max_depth mean_max_depth
   <fct>       <chr>              <dbl>          <dbl>
 1 N. Pacific  1994 - 2004           NA            200
 2 N. Pacific  2004 - 2014           NA            150
 3 S. Pacific  1994 - 2004           NA            400
 4 S. Pacific  2004 - 2014           NA            400
 5 N. Atlantic 1994 - 2004           NA            400
 6 N. Atlantic 2004 - 2014           NA            400
 7 S. Atlantic 1994 - 2004           NA            250
 8 S. Atlantic 2004 - 2014           NA            400
 9 Indian      1994 - 2004           NA            400
10 Indian      2004 - 2014           NA            250
11 Global      1994 - 2004           NA            300
12 Global      2004 - 2014           NA            300

full_join(dcant_profile_basin_MLR_all_ensemble,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(dcant >= mean_surface_dcant/2) %>%
  group_by(period, basin, Version_ID) %>% 
  summarise(max_depth = max(depth)) %>% 
  ungroup() %>% 
  group_by(basin, period) %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth)) %>% 
  ungroup()

# A tibble: 12 × 4
   basin       period      sd_max_depth mean_max_depth
   <fct>       <chr>              <dbl>          <dbl>
 1 N. Pacific  1994 - 2004         62.4           260.
 2 N. Pacific  2004 - 2014         67.6           200 
 3 S. Pacific  1994 - 2004        190.            411.
 4 S. Pacific  2004 - 2014         90.9           328.
 5 N. Atlantic 1994 - 2004         89.3           397.
 6 N. Atlantic 2004 - 2014        134.            481.
 7 S. Atlantic 1994 - 2004         67.4           303.
 8 S. Atlantic 2004 - 2014        237.            438.
 9 Indian      1994 - 2004        144.            478.
10 Indian      2004 - 2014        119.            264.
11 Global      1994 - 2004        112.            350 
12 Global      2004 - 2014         37.8           292.

dcant_profile_basin_MLR_range <- dcant_profile_basin_MLR_all_ensemble %>%
  # filter(period != "1994 - 2014") %>%
  group_by(basin, depth, period, data_source) %>% 
  summarise(dcant_sd = sd(dcant)*2) %>%
  ungroup()

dcant_profile_basin_MLR_all_ensemble <-
full_join(dcant_profile_basin_MLR_all_ensemble %>% select(-dcant_sd),
          dcant_profile_basin_MLR_range)

p_dcant_profiles <-
  dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source == "obs",
    Version_ID %in% Version_IDs
  ) %>%
  ggplot() +
  geom_vline(xintercept = 0, size = 0.1) +
  geom_hline(yintercept = 1000, size = 0.1) +
  geom_ribbon(
    aes(
      xmin = dcant - dcant_sd,
      xmax = dcant + dcant_sd,
      y = depth,
      fill = period,
      group = Version_ID
    ),
    alpha = 0.5
  ) +
  geom_path(
    aes(dcant,
        depth,
        col = period,
        group = Version_ID,)
  ) +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(0, 100, 500, seq(1000, 5000, 1000))) +
  coord_cartesian(expand = 0) +
  scale_color_manual(values = c("#EE7733", "#009988"), name = "Mean \u00B1 2 SD") +
  scale_fill_manual(values = c("#EE7733", "#009988"), name = "Mean \u00B1 2 SD") +
  labs(y = "Depth (m)",
       x = dcant_umol_label) +
  facet_wrap(~ basin, ncol = 3, dir = "v") +
  theme(legend.position = c(0.9, 0.1))

p_dcant_profiles

Past versions of concentration_profiles_MLR_basins_ensemble_uncertainty_ribbon-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
f1d7f80	jens-daniel-mueller	2022-07-13

mean_surface_dcant <- dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(depth <= 50) %>%
  group_by(basin, period) %>% 
  summarise(mean_surface_dcant = mean(dcant)) %>% 
  ungroup()

full_join(dcant_profile_basin_MLR_all,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(dcant >= mean_surface_dcant/2) %>%
  group_by(period, basin, Version_ID) %>% 
  summarise(max_depth = max(depth)) %>% 
  ungroup() %>% 
  group_by(basin, period) %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth)) %>% 
  ungroup()

# A tibble: 12 × 4
   basin       period      sd_max_depth mean_max_depth
   <fct>       <chr>              <dbl>          <dbl>
 1 N. Pacific  1994 - 2004           NA            200
 2 N. Pacific  2004 - 2014           NA            150
 3 S. Pacific  1994 - 2004           NA            400
 4 S. Pacific  2004 - 2014           NA            400
 5 N. Atlantic 1994 - 2004           NA            400
 6 N. Atlantic 2004 - 2014           NA            400
 7 S. Atlantic 1994 - 2004           NA            250
 8 S. Atlantic 2004 - 2014           NA            400
 9 Indian      1994 - 2004           NA            400
10 Indian      2004 - 2014           NA            250
11 Global      1994 - 2004           NA            300
12 Global      2004 - 2014           NA            300

full_join(dcant_profile_basin_MLR_all_ensemble,
          mean_surface_dcant) %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         data_source == "obs") %>% 
  filter(dcant >= mean_surface_dcant/2) %>%
  group_by(period, basin, Version_ID) %>% 
  summarise(max_depth = max(depth)) %>% 
  ungroup() %>% 
  group_by(basin, period) %>% 
  summarise(sd_max_depth = sd(max_depth)*2,
            mean_max_depth = mean(max_depth)) %>% 
  ungroup()

# A tibble: 12 × 4
   basin       period      sd_max_depth mean_max_depth
   <fct>       <chr>              <dbl>          <dbl>
 1 N. Pacific  1994 - 2004         62.4           260.
 2 N. Pacific  2004 - 2014         67.6           200 
 3 S. Pacific  1994 - 2004        190.            411.
 4 S. Pacific  2004 - 2014         90.9           328.
 5 N. Atlantic 1994 - 2004         89.3           397.
 6 N. Atlantic 2004 - 2014        134.            481.
 7 S. Atlantic 1994 - 2004         67.4           303.
 8 S. Atlantic 2004 - 2014        237.            438.
 9 Indian      1994 - 2004        144.            478.
10 Indian      2004 - 2014        119.            264.
11 Global      1994 - 2004        112.            350 
12 Global      2004 - 2014         37.8           292.

p_dcant_profiles_mod <-
  dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source != "obs",
    Version_ID %in% Version_IDs
  ) %>%
    mutate(data_source = case_when(
    data_source == "mod" ~ "Reconstruction",
    data_source == "mod_truth" ~ "Model truth"
  )) %>%  
  ggplot() +
  geom_vline(xintercept = 0, size = 0.1) +
  geom_hline(yintercept = 1000, size = 0.1) +
  geom_ribbon(
    aes(
      xmin = dcant - dcant_sd,
      xmax = dcant + dcant_sd,
      y = depth,
      fill = data_source,
      group = interaction(Version_ID,data_source)
    ),
    alpha = 0.5
  ) +
  geom_path(
    aes(dcant,
        depth,
        col = data_source,
        group = interaction(Version_ID,data_source))
  ) +
  scale_y_continuous(trans = trans_reverser("sqrt"),
                     breaks = c(0, 100, 500, seq(1000, 5000, 1000))) +
  coord_cartesian(expand = 0) +
  scale_color_manual(values = c("#009988", "#EE7733"), name = "Mean \u00B1 2 SD") +
  scale_fill_manual(values = c("#009988", "#EE7733"), name = "Mean \u00B1 2 SD") +
  labs(y = "Depth (m)",
       x = dcant_umol_label) +
  facet_grid(period ~ basin) +
  theme(legend.position = "top")

p_dcant_profiles_mod

Past versions of concentration_profiles_MLR_basins_ensemble_uncertainty_ribbon_mod-1.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11

dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard,
    data_source == "obs"
  ) %>%
  group_split(data_source) %>%
  map(
    ~ ggplot() +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path(
        data = .x,
        aes(dcant,
            depth,
            col = MLR_basins,
            group = Version_ID),
        alpha = 0.5
      ) +
      scale_size_manual(values = c(0.5, 2), name = "x") +
      scale_linetype_manual(values = c(2, 1), name = "x") +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(palette = "Dark2") +
      labs(y = "Depth (m)",
           x = dcant_umol_label) +
      facet_grid(basin ~ period)
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_basin_separation-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_profile_basin_MLR_all_ensemble %>%
  arrange(depth) %>%
  filter(
    # period != "1994 - 2014",
    depth <= params_global$inventory_depth_standard
    # data_source == "obs"
  ) %>%
  group_split(data_source) %>%
  # head(1) %>% 
  map(
    ~ ggplot() +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_path(
        data = .x,
        aes(
          dcant,
          depth,
          col = Version_ID_group,
          group = Version_ID
        ), alpha = 0.5
      ) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(
        palette = "Dark2",
        direction = -1
      ) +
      scale_fill_brewer(
        palette = "Set1",
        direction = -1
      ) +
      labs(
        y = "Depth (m)",
        x = dcant_umol_label,
        title = unique(.x$data_source)
      ) +
      facet_grid(basin ~ period)
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_ensemble_member-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
6be73e0	jens-daniel-mueller	2022-06-30

[[2]]

Past versions of concentration_profiles_MLR_basins_ensemble_member-2.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
6be73e0	jens-daniel-mueller	2022-06-30

[[3]]

Past versions of concentration_profiles_MLR_basins_ensemble_member-3.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11

dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014",
         depth <= params_global$inventory_depth_standard) %>%
  group_split(data_source, basin) %>%
  # head(1) %>%
  map(
    ~ ggplot(data = .x,
             aes(dcant,
                 depth)) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_ribbon(
        aes(
          xmin = dcant - dcant_sd,
          xmax = dcant + dcant_sd,
          fill = period
        ),
        alpha = 0.3
      ) +
      geom_path(aes(col = period)) +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(palette = "Set1", name = "mean \u00B1 sd", direction = -1) +
      scale_fill_brewer(palette = "Set1", name = "mean \u00B1 sd", direction = -1) +
      labs(
        title = paste("data_source", unique(.x$data_source),
                      "|", unique(.x$basin)),
        y = "Depth (m)",
        x = dcant_umol_label
      ) +
      # facet_wrap(~ basin, ncol = 3, dir = "v") +
      theme(legend.position = c(0.8,0.2))
  )

9.3 dcant - absolute delta

delta <- dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014") %>%
  select(data_source, depth, basin, period, dcant) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(delta_dcant = `2004 - 2014` - `1994 - 2004`) %>%
  select(-c(`2004 - 2014`, `1994 - 2004`))

delta_sd <- dcant_profile_basin_MLR_all %>%
  arrange(depth) %>%
  filter(period != "1994 - 2014") %>%
  select(data_source, depth, basin, period, dcant_sd) %>% 
  pivot_wider(names_from = period,
              values_from = dcant_sd) %>%
  mutate(delta_dcant_sd = sqrt(`2004 - 2014`^2 + `1994 - 2004`^2)) %>% 
  select(-c(`2004 - 2014`, `1994 - 2004`))

dcant_profile_basin_MLR_all_delta <- full_join(delta, delta_sd)
rm(delta, delta_sd)



dcant_profile_basin_MLR_all_delta %>%
  group_split(data_source) %>%
  # head(3) %>%
  map(
    ~ ggplot(data = .x,
             aes(delta_dcant,
                 depth)) +
      geom_hline(yintercept = params_global$inventory_depth_standard) +
      geom_vline(xintercept = 0) +
      geom_ribbon(
        aes(
          xmin = delta_dcant - delta_dcant_sd,
          xmax = delta_dcant + delta_dcant_sd
        ),
        alpha = 0.3
      ) +
      geom_path() +
      scale_y_continuous(trans = trans_reverser("sqrt"),
                         breaks = c(0, 100, 500, seq(1500, 5000, 1000))) +
      coord_cartesian(expand = 0) +
      scale_color_brewer(palette = "Set1", name = "mean \u00B1 sd") +
      scale_fill_brewer(palette = "Set1", name = "mean \u00B1 sd") +
      labs(
        title = paste("data_source", unique(.x$data_source)),
        y = "Depth (m)",
        x = ddcant_umol_label
      ) +
      facet_wrap(~ basin, ncol = 3)
  )

[[1]]

Past versions of concentration_profiles_MLR_basins_offset-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
eccd82b	jens-daniel-mueller	2022-01-26
c6fe495	jens-daniel-mueller	2022-01-26

[[2]]

Past versions of concentration_profiles_MLR_basins_offset-2.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
eccd82b	jens-daniel-mueller	2022-01-26
c6fe495	jens-daniel-mueller	2022-01-26

[[3]]

Past versions of concentration_profiles_MLR_basins_offset-3.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11

10 Budgets

dcant_budget_global_all_ensemble <- dcant_budget_global_all_ensemble %>% 
  mutate(dcant = dcant * dcant_scaling)

dcant_budget_global_all <- dcant_budget_global_all %>% 
  mutate(dcant = dcant * dcant_scaling)

dcant_budget_global_all_no_adj <- dcant_budget_global_all_no_adj %>% 
  mutate(dcant = dcant * dcant_scaling)

dcant_budget_basin_MLR_all_ensemble <- dcant_budget_basin_MLR_all_ensemble %>% 
  mutate(dcant = if_else(basin == "Global",
                         dcant * dcant_scaling,
                         dcant))

dcant_budget_basin_MLR_all <- dcant_budget_basin_MLR_all %>% 
  mutate(dcant = if_else(basin == "Global",
                         dcant * dcant_scaling,
                         dcant))

dcant_budget_basin_MLR_all_no_adj <- dcant_budget_basin_MLR_all_no_adj %>% 
  mutate(dcant = if_else(basin == "Global",
                         dcant * dcant_scaling,
                         dcant))

dcant_budget_ensemble_stat <-
  bind_rows(
    dcant_budget_basin_MLR_all_ensemble
  ) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  group_by(period, basin) %>%
  summarise(
    median = median(dcant),
    sd = sd(dcant),
    n = n()
  ) %>%
  ungroup() 


dcant_budget_ensemble_stat <-
  full_join(
    dcant_budget_ensemble_stat,
    dcant_budget_basin_MLR_all_ensemble %>%
        filter(Version_ID %in% Version_IDs,
               data_source == "obs",
               period != "1994 - 2014") %>%
        select(period,
               basin,
               std_case = dcant)
  )

dcant_budget_ensemble_significance <- dcant_budget_ensemble_stat %>% 
  pivot_longer(c(median,std_case),
               names_to = "estimate",
               values_to = "dcant") %>%
  group_by(estimate, basin) %>%
  mutate(
    delta_dcant = dcant - lag(dcant),
    delta_dcant_uncert = sqrt(2 * sd ^ 2 + lag(2 * sd ^ 2)),
    error_prop_ratio = abs(delta_dcant_uncert / delta_dcant),
    ttest_p = tsum.test(
      mean.x = dcant,
      s.x = sd,
      n.x = n,
      mean.y = lag(dcant),
      s.y = lag(sd),
      n.y = lag(n)
    )$p.value
  ) %>%
  ungroup() %>% 
  drop_na()
  
dcant_budget_ensemble_stat_significance <-
  full_join(
    dcant_budget_ensemble_stat %>%
      select(-n) %>%
      pivot_wider(
        names_from = period,
        values_from = median:std_case,
        names_sep = " "
      ),
    dcant_budget_ensemble_significance %>%
      select(basin, estimate, delta_dcant, delta_dcant_uncert, ttest_p) %>%
      pivot_wider(names_from = estimate,
                  values_from = delta_dcant:ttest_p) %>%
      rename(delta_dcant_uncert = delta_dcant_uncert_std_case) %>%
      select(-delta_dcant_uncert_median)
  )


dcant_budget_ensemble_stat_significance %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	median 1994 - 2004	median 2004 - 2014	sd 1994 - 2004	sd 2004 - 2014	std_case 1994 - 2004	std_case 2004 - 2014	delta_dcant_median	delta_dcant_std_case	delta_dcant_uncert	ttest_p_median	ttest_p_std_case
N. Pacific	2.86900	3.54000	0.9932880	0.8096496	2.20900	3.80200	0.671000	1.5930000	1.812266	0.0024972	0.0000000
S. Pacific	8.80550	7.63700	1.2820208	0.9030507	8.39900	7.61700	-1.168500	-0.7820000	2.217691	0.0000333	0.0039553
N. Atlantic	4.52250	3.65850	0.2466070	0.3747788	4.51400	3.46500	-0.864000	-1.0490000	0.634467	0.0000000	0.0000000
S. Atlantic	4.08700	4.89750	0.5931842	0.5702755	2.92700	5.18100	0.810500	2.2540000	1.163685	0.0000001	0.0000000
Indian	6.87100	5.74200	0.7230791	0.7783228	7.07200	5.73900	-1.129000	-1.3330000	1.502418	0.0000000	0.0000000
Global	28.95887	27.24436	2.3997433	1.6597223	26.79321	27.52167	-1.714505	0.7284646	4.126366	0.0007990	0.1391916

dcant_budget_ensemble_significance %>% 
  ggplot(aes(error_prop_ratio, ttest_p, col = basin)) +
  geom_vline(xintercept = 1) +
  geom_hline(yintercept = 0.05) +
  geom_point() +
  scale_y_log10() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ estimate)

Past versions of dcant_budget_stats-1.png

Version	Author	Date
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
7267b08	jens-daniel-mueller	2022-07-16

# estimate area scaling factor to achieve identical coverage

tcant_inv <-
  inner_join(tcant_inv, basinmask)

dcant_inv_all <-
  inner_join(dcant_inv_all, basinmask)

area_scaling <- 
bind_rows(
  tcant_inv %>% distinct(lat, lon, basin) %>%
    mutate(source = "SO4"),
  dcant_inv_all %>% distinct(lat, lon, basin) %>%
    mutate(source = "M22")
)


map +
  geom_tile(data = area_scaling,
            aes(lon, lat, fill = basin)) +
  facet_wrap( ~ source)

Past versions of budgets_basins_hemisphere_tcant_for_scatterplot-1.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
d2ae54c	jens-daniel-mueller	2022-07-18

area_scaling <- area_scaling %>% 
  mutate(area = earth_surf(lat, lon)) %>% 
  group_by(basin, source) %>% 
  summarise(area_total = sum(area)) %>% 
  ungroup() %>% 
  pivot_wider(values_from = area_total,
              names_from = source) %>% 
  mutate(scaling_factor = M22 / SO4)


tcant_budget_basin_MLR <-
  tcant_inv %>%
  mutate(method = "layer",
         data_source = "obs") %>% 
  group_by(basin) %>%
  nest() %>% 
  mutate(budget = map(.x = data, ~m_dcant_budget(.x))) %>% 
  select(-data) %>%
  unnest(budget)

tcant_budget_basin_MLR <-
  full_join(tcant_budget_basin_MLR,
            area_scaling %>% select(basin, scaling_factor)) %>%
  mutate(value = value * scaling_factor) %>%
  select(-scaling_factor)



tcant_budget_basin_MLR <-
  left_join(tcant_budget_basin_MLR %>%
              mutate(period = "1800 - 1994"),
            delta_pCO2_atm)


tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  filter(estimate == "dcant") %>% 
  select(-estimate) %>% 
  rename(dcant = value)

tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  mutate(beta_1994 = dcant / delta_pCO2) %>% 
  select(basin, beta_1994)

tcant_budget_basin_MLR <-
  bind_rows(tcant_budget_basin_MLR,
            tibble(basin = "Global", beta_1994 = 118 / 78.3))

10.1 Scatterplots

10.1.1 Global

bind_rows(dcant_budget_global_all_no_adj,
          dcant_budget_global_all_ensemble) %>%
  ggplot(aes(period, dcant, col = Version_ID_group)) +
  geom_jitter() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ data_source)

Past versions of dcant_budget_global_ensemble-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
26e9496	jens-daniel-mueller	2022-07-12
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
6be73e0	jens-daniel-mueller	2022-06-30

dcant_budget_global_all_ensemble_bias <- 
dcant_budget_global_all_ensemble %>%
  filter(data_source != "obs") %>% 
  pivot_wider(names_from = data_source,
              values_from = dcant) %>% 
  mutate(dcant_bias = mod - mod_truth,
         dcant_bias_rel = 100*(mod - mod_truth)/mod_truth)

dcant_budget_global_all_ensemble_bias %>% 
  ggplot(aes(period, dcant_bias, col = MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_jitter(width = 0.2) +
  scale_color_brewer(palette = "Dark2")

Past versions of dcant_budget_global_ensemble-2.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
535196a	jens-daniel-mueller	2022-07-17
0160c40	jens-daniel-mueller	2022-07-16
efa414b	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
26e9496	jens-daniel-mueller	2022-07-12
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
6be73e0	jens-daniel-mueller	2022-06-30

dcant_budget_global_all_ensemble_bias %>% 
  ggplot(aes(period, dcant_bias_rel, col = MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_jitter(width = 0.2) +
  scale_color_brewer(palette = "Dark2")

Past versions of dcant_budget_global_ensemble-3.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble_bias %>% 
  group_by(period) %>% 
  summarise(dcant_bias_rel = mean(dcant_bias_rel)) %>% 
  ungroup()

# A tibble: 3 × 2
  period      dcant_bias_rel
  <chr>                <dbl>
1 1994 - 2004           7.64
2 1994 - 2014           6.56
3 2004 - 2014           5.78

delta_dcant_budget_global_all_ensemble <- 
dcant_budget_global_all_ensemble %>%
  filter(data_source != "obs",
         period != "1994 - 2014") %>% 
  select(data_source, MLR_basins, Version_ID_group, tref1, dcant) %>% 
  pivot_wider(names_from = tref1,
              values_from = dcant) %>% 
  mutate(delta_dcant = `2004` - `1994`)

delta_dcant_budget_global_all_ensemble %>% 
  ggplot(aes(data_source, delta_dcant, col = MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_jitter(width = 0.2) +
  scale_color_brewer(palette = "Dark2")

Past versions of dcant_budget_global_ensemble-4.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16

delta_dcant_budget_global_all_ensemble_bias <- 
delta_dcant_budget_global_all_ensemble %>%
  select(data_source, MLR_basins, Version_ID_group, delta_dcant) %>% 
  pivot_wider(names_from = data_source,
              values_from = delta_dcant) %>% 
  mutate(delta_dcant_bias = mod - mod_truth,
         delta_dcant_bias_rel = 100*(mod - mod_truth)/mod_truth)


delta_dcant_budget_global_all_ensemble_bias %>% 
  summarise(delta_dcant_bias = mean(delta_dcant_bias),
            delta_dcant_bias_rel = mean(delta_dcant_bias_rel))

# A tibble: 1 × 2
  delta_dcant_bias delta_dcant_bias_rel
             <dbl>                <dbl>
1           -0.153                -4.49

delta_dcant_budget_global_all_ensemble_bias %>% 
  group_by(MLR_basins) %>% 
  summarise(delta_dcant_bias = mean(delta_dcant_bias),
            delta_dcant_bias_rel = mean(delta_dcant_bias_rel)) %>% 
  ungroup()

# A tibble: 6 × 3
  MLR_basins delta_dcant_bias delta_dcant_bias_rel
  <chr>                 <dbl>                <dbl>
1 1                    0.0645                 1.90
2 2                    1.89                  55.7 
3 3                    1.08                  31.7 
4 3+SO                -1.46                 -43.0 
5 5                   -0.675                -19.9 
6 5+SO                -1.81                 -53.4 

delta_dcant_budget_global_all_ensemble_bias %>% 
  ggplot(aes("Decade difference",delta_dcant_bias, col = MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_jitter(width = 0.2) +
  scale_color_brewer(palette = "Dark2") +
  theme(axis.title.x = element_blank())

Past versions of dcant_budget_global_ensemble-5.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17

delta_dcant_budget_global_all_ensemble_bias %>% 
  ggplot(aes("Decade difference",delta_dcant_bias_rel, col = MLR_basins)) +
  geom_hline(yintercept = 0) +
  geom_jitter(width = 0.2) +
  scale_color_brewer(palette = "Dark2") +
  theme(axis.title.x = element_blank())

Past versions of dcant_budget_global_ensemble-6.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17

dcant_budget_global_all_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014",
         !(Version_ID %in% Version_IDs)) %>%
  ggplot(aes(period, dcant)) +
  scale_fill_manual(values = c("grey50", "red", "white")) +
  geom_point(
    data = dcant_budget_global_all_no_adj %>%
      filter(data_source == "obs",
             period != "1994 - 2014"),
    aes(fill = "Unadjusted data"),
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  geom_point(
    aes(fill = "Ensemble member"),
    alpha = .5,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  geom_linerange(
    data = dcant_budget_ensemble_stat %>%
      filter(basin == "Global"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd
    ),
    col = "red"
  ) +
  geom_point(
    data = dcant_budget_ensemble_stat %>%
      filter(basin == "Global"),
    aes(x = period,
        y = std_case,
        fill = "Standard case"),
    size = 2,
    shape = 21
  ) +
  scale_y_continuous(name = dcant_pgc_label, breaks = seq(0, 100, 2)) +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank(),
        legend.position = c(0.65,0.85),
        legend.background = element_rect(colour = "grey"))

Past versions of dcant_budget_global_ensemble-7.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17

dcant_budget_global_all_ensemble %>%
  mutate(ensemble_role = case_when(Version_ID %in% Version_IDs ~ "Standard case",
                                   TRUE ~ "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
  scale_fill_brewer(palette = "Dark2", name = "ensemble group") +
  scale_color_manual(values = c("black", "red", "grey"),
                     name = "Ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    data = dcant_budget_global_all_no_adj %>%
      mutate(ensemble_role = "unadjusted") %>%
      filter(data_source == "obs",
             period != "1994 - 2014"),
    aes(col = ensemble_role),
    size = 2,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 2,
    alpha = .5,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_label, breaks = seq(0, 100, 2))

Past versions of dcant_budget_global_ensemble-8.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18

dcant_budget_global_all_ensemble %>%
  mutate(ensemble_role = if_else(Version_ID %in% Version_IDs,
                                 "Standard case",
                                 "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
  scale_fill_brewer(palette = "Dark2") +
  scale_color_manual(values = c("grey", "red"),
                     name = "Ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    aes(group = Version_ID_group,
        fill = MLR_basins,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  stat_summary(
    fun.data = "mean_sdl",
    fun.args = list(mult = 2),
    position = position_nudge(x = -0.3),
    col = "grey"
  ) +
  stat_summary(
    fun.data = "mean_sdl",
    fun.args = list(mult = 1),
    position = position_nudge(x = -0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_label, breaks = seq(0, 100, 2))

Past versions of dcant_budget_global_ensemble-9.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18

beta_budget_global_all_ensemble <-
  full_join(dcant_budget_global_all_ensemble,
            delta_pCO2_atm) %>%
  mutate(beta = dcant / delta_pCO2)
  
  
beta_budget_global_all_ensemble %>%   
  mutate(ensemble_role = case_when(Version_ID %in% Version_IDs ~ "Standard case",
                                   TRUE ~ "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, beta)) +
  scale_fill_brewer(palette = "Dark2", name = "ensemble group") +
  scale_color_manual(values = c("black", "red", "grey"),
                     name = "Ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 2,
    alpha = .5,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_scaled_label)

Past versions of beta_budget_global-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
022fd60	jens-daniel-mueller	2022-07-14

10.1.2 basin hemisphere

dcant_budget_basin_MLR_all_ensemble %>%
  ggplot(aes(period, dcant, col = Version_ID_group)) +
  geom_jitter(alpha = 0.5) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ data_source, scales = "free_y")

Past versions of dcant_budget_basin_hemisphere_ensemble-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
df21d31	jens-daniel-mueller	2022-07-01
6be73e0	jens-daniel-mueller	2022-06-30

tcant_budget_basin_MLR <- expand_grid(
  tcant_budget_basin_MLR,
  delta_pCO2_atm %>% filter(period %in% two_decades)
)

tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  mutate(dcant = beta_1994 * delta_pCO2)

tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>%
  mutate(
    basin = fct_relevel(
      basin,
      "N. Pacific",
      "S. Pacific",
      "N. Atlantic",
      "S. Atlantic",
      "Indian",
      "Global"
    )
  )

dcant_budget_basin_MLR_all_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014",
         !(Version_ID %in% Version_IDs)) %>%
  ggplot(aes(period, dcant)) +
  geom_errorbar(
    data = tcant_budget_basin_MLR,
    aes(x = period,
        ymax = dcant, ymin = dcant,
        col = "Scaled 1994\nestimate"),
    width = 0.5,
    linetype = 2,
    position = position_nudge(x = 0.1)
  ) +
  geom_jitter(
    data = dcant_budget_basin_MLR_all_no_adj %>%
      filter(data_source == "obs",
             period != "1994 - 2014"),
    aes(fill = "Unadjusted data",
        alpha = "Unadjusted data"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_point(
    aes(fill = "Ensemble member",
        alpha = "Ensemble member"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_crossbar(
    data = dcant_budget_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      ymin = std_case - 2*sd,
      ymax = std_case + 2*sd,
      linetype = "Ensemble SD"
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = dcant_budget_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd,
      alpha = "Ensemble SD",
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = dcant_budget_ensemble_stat,
    aes(x = period,
        y = std_case,
        fill = "Standard case",
        alpha = "Standard case"),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  ) +
  scale_fill_manual(name = "group", values = c("#BB5566", "white", "#BBBBBB")) +
  scale_color_manual(values = c("grey50"), label = expression(Scaled~C[ant]~1994)) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.5,1,0.5)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = dcant_pgc_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank())

Past versions of dcant_budget_basin_hemisphere_ensemble-2.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
022fd60	jens-daniel-mueller	2022-07-14
17cd1d1	jens-daniel-mueller	2022-07-13
26e9496	jens-daniel-mueller	2022-07-12
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
df21d31	jens-daniel-mueller	2022-07-01
6be73e0	jens-daniel-mueller	2022-06-30

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_budgets_ensemble_member.png",
       height = 6,
       width = 8)



dcant_budget_basin_MLR_all_ensemble %>%
  mutate(ensemble_role = if_else(Version_ID %in% Version_IDs,
                                 "Standard case",
                                 "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
  scale_fill_brewer(palette = "Dark2", name = "ensemble group") +
  scale_color_manual(values = c("grey", "black", "red"),
                     name = "Ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    data = dcant_budget_basin_MLR_all_no_adj %>%
      mutate(ensemble_role = "no adjustment") %>%
      filter(data_source == "obs",
         period != "1994 - 2014"),
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_label) +
  facet_wrap(~ basin, ncol = 3, scales = "free_y")

Past versions of dcant_budget_basin_hemisphere_ensemble-3.png

Version	Author	Date
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
022fd60	jens-daniel-mueller	2022-07-14
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_basin_MLR_all_ensemble %>%
   mutate(ensemble_role = if_else(Version_ID %in% Version_IDs,
                                 "Standard case",
                                 "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
  scale_fill_brewer(palette = "Dark2") +
  scale_color_manual(values = c("grey", "red"),
                     name = "Ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    aes(fill = MLR_basins,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  stat_summary(
    fun.data = "mean_sdl",
    fun.args = list(mult = 2),
    position = position_nudge(x = -0.3),
    col = "grey"
  ) +
  stat_summary(
    fun.data = "mean_sdl",
    fun.args = list(mult = 1),
    position = position_nudge(x = -0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_label) +
  facet_wrap(~ basin, ncol = 3, scales = "free_y")

Past versions of dcant_budget_basin_hemisphere_ensemble-4.png

Version	Author	Date
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17

dcant_budget_basin_MLR_all_ensemble_summary <- dcant_budget_basin_MLR_all_ensemble %>% 
  group_by(data_source, basin, period) %>% 
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant)*2) %>% 
  ungroup()

dcant_budget_global_all_ensemble_summary <-
  dcant_budget_basin_MLR_all_ensemble %>%
  filter(period != "1994 - 2014") %>%
  group_by(data_source, Version_ID, tref1, tref2, period) %>%
  summarise(dcant = sum(dcant)) %>%
  ungroup() %>%
  group_by(data_source, tref1, tref2, period) %>%
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant)*2) %>%
  ungroup() %>%
  filter(data_source == "obs")

beta_budget_basin_MLR_all_no_adj <-
  full_join(dcant_budget_basin_MLR_all_no_adj,
            delta_pCO2_atm) %>%
  mutate(beta = dcant / delta_pCO2)

beta_budget_basin_MLR_all_ensemble <-
  full_join(dcant_budget_basin_MLR_all_ensemble,
            delta_pCO2_atm) %>%
  mutate(beta = dcant / delta_pCO2)

beta_budget_basin_MLR_all_ensemble %>% 
  mutate(ensemble_role = if_else(Version_ID %in% Version_IDs,
                                 "Standard case",
                                 "others")) %>%
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  ggplot(aes(period, beta)) +
  scale_fill_brewer(palette = "Dark2", name = "ensemble group") +
  scale_color_manual(values = c("grey", "black", "red"),
                     name = "Ensemble member") +
  geom_boxplot(
    width = .1,
    outlier.shape = NA,
    fill = "grey",
    alpha = 0.7,
    position = position_nudge(x = 0.3)
  ) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  scale_y_continuous(name = dcant_pgc_scaled_label)  +
  facet_wrap(~ basin, ncol = 3, scales = "free_y")

Past versions of beta_budget_basin_hemisphere_ensemble-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
022fd60	jens-daniel-mueller	2022-07-14

beta_budget_ensemble_stat_KS <-
  beta_budget_basin_MLR_all_ensemble %>% 
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  select(basin, period, dcant, beta, MLR_basins, Version_ID_group) %>% 
  pivot_longer(c(dcant, beta),
               values_to = "value",
               names_to = "estimate") %>% 
  pivot_wider(names_from = period,
              values_from = value)

beta_budget_ensemble_stat_KS %>%
  group_by(basin, estimate) %>%
  summarise(
    ks_p = ks.test(`2004 - 2014`, `1994 - 2004`)$p.value,
    t_p = t.test(`2004 - 2014`, `1994 - 2004`)$p.value
  ) %>%
  ungroup() %>% 
  mutate(ratio_p = ks_p / t_p)

# A tibble: 12 × 5
   basin       estimate     ks_p      t_p ratio_p
   <fct>       <chr>       <dbl>    <dbl>   <dbl>
 1 N. Pacific  beta     2.11e- 1 2.52e- 1 8.36e-1
 2 N. Pacific  dcant    6.53e- 4 1.07e- 2 6.10e-2
 3 S. Pacific  beta     7.06e-13 9.67e-13 7.30e-1
 4 S. Pacific  dcant    5.41e-11 4.39e- 8 1.23e-3
 5 N. Atlantic beta     4.44e-16 7.90e-26 5.62e+9
 6 N. Atlantic dcant    5.10e-12 6.53e-18 7.81e+5
 7 S. Atlantic beta     3.49e- 3 6.93e- 2 5.04e-2
 8 S. Atlantic dcant    8.83e- 5 1.07e- 5 8.27e+0
 9 Indian      beta     1.37e- 8 1.26e- 9 1.08e+1
10 Indian      dcant    8.83e- 5 4.18e- 4 2.11e-1
11 Global      beta     6.66e-12 1.69e-13 3.95e+1
12 Global      dcant    1.41e- 3 5.21e- 5 2.71e+1

beta_budget_ensemble_stat <-
  beta_budget_basin_MLR_all_ensemble %>% 
  filter(data_source == "obs",
         period != "1994 - 2014") %>%
  select(basin, period, dcant, beta) %>% 
  pivot_longer(c(dcant, beta),
               values_to = "value",
               names_to = "estimate") %>% 
  group_by(period, basin, estimate) %>%
  summarise(
    # mean = mean(beta),
    median = median(value),
    sd = sd(value),
    n = n()
  ) %>%
  ungroup() 


beta_budget_ensemble_stat <-
  full_join(
    beta_budget_ensemble_stat,
    beta_budget_basin_MLR_all_ensemble %>%
      filter(
        Version_ID %in% Version_IDs,
        data_source == "obs",
        period != "1994 - 2014"
      ) %>%
      select(period,
             basin,
             dcant,
             beta) %>% 
        pivot_longer(c(dcant, beta),
               values_to = "std_case",
               names_to = "estimate")
  )


beta_budget_ensemble_significance <- beta_budget_ensemble_stat %>% 
  pivot_longer(c(median,std_case),
               names_to = "case",
               values_to = "value") %>%
  group_by(estimate, basin, case) %>%
  mutate(
    delta = value - lag(value),
    RSS = sqrt(2 * sd ^ 2 + lag(2 * sd ^ 2)),
    RSS_delta_ratio = abs(delta) / RSS,
    ttest_p = tsum.test(
      mean.x = value,
      s.x = sd,
      n.x = n,
      mean.y = lag(value),
      s.y = lag(sd),
      n.y = lag(n)
    )$p.value
  ) %>%
  ungroup() %>% 
  drop_na()
  

beta_budget_ensemble_stat_significance <-
  full_join(
    beta_budget_ensemble_stat %>%
      select(-n) %>%
      pivot_wider(
        names_from = period,
        values_from = median:std_case,
        names_sep = " "
      ),
    beta_budget_ensemble_significance %>%
      select(basin, estimate, case, delta, RSS, ttest_p) %>%
      pivot_wider(names_from = case,
                  values_from = c(delta, ttest_p))
  )

beta_budget_ensemble_stat_significance <-
  beta_budget_ensemble_stat_significance %>% 
  mutate(across(where(is.numeric), signif, 2))

beta_budget_ensemble_stat_significance %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	estimate	median 1994 - 2004	median 2004 - 2014	sd 1994 - 2004	sd 2004 - 2014	std_case 1994 - 2004	std_case 2004 - 2014	RSS	delta_median	delta_std_case	ttest_p_median	ttest_p_std_case
N. Pacific	beta	0.15	0.17	0.053	0.040	0.12	0.19	0.094	0.020	0.068	8.3e-02	1.0e-07
N. Pacific	dcant	2.90	3.50	0.990	0.810	2.20	3.80	1.800	0.670	1.600	2.5e-03	0.0e+00
S. Pacific	beta	0.47	0.37	0.069	0.044	0.45	0.37	0.120	-0.098	-0.078	0.0e+00	4.0e-07
S. Pacific	dcant	8.80	7.60	1.300	0.900	8.40	7.60	2.200	-1.200	-0.780	3.3e-05	4.0e-03
N. Atlantic	beta	0.24	0.18	0.013	0.018	0.24	0.17	0.032	-0.063	-0.072	0.0e+00	0.0e+00
N. Atlantic	dcant	4.50	3.70	0.250	0.370	4.50	3.50	0.630	-0.860	-1.000	0.0e+00	0.0e+00
S. Atlantic	beta	0.22	0.24	0.032	0.028	0.16	0.25	0.060	0.021	0.097	4.6e-03	0.0e+00
S. Atlantic	dcant	4.10	4.90	0.590	0.570	2.90	5.20	1.200	0.810	2.300	1.0e-07	0.0e+00
Indian	beta	0.37	0.28	0.039	0.038	0.38	0.28	0.077	-0.087	-0.098	0.0e+00	0.0e+00
Indian	dcant	6.90	5.70	0.720	0.780	7.10	5.70	1.500	-1.100	-1.300	0.0e+00	0.0e+00
Global	beta	1.60	1.30	0.130	0.081	1.40	1.30	0.220	-0.220	-0.089	0.0e+00	8.6e-04
Global	dcant	29.00	27.00	2.400	1.700	27.00	28.00	4.100	-1.700	0.730	8.0e-04	1.4e-01

beta_budget_ensemble_stat_significance %>% 
  ggplot(aes(abs(delta_std_case) / RSS, ttest_p_std_case, col = basin)) +
  geom_vline(xintercept = 1) +
  geom_hline(yintercept = 0.05) +
  geom_point() +
  scale_y_log10() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ estimate)

Past versions of beta_budget_stats-1.png

Version	Author	Date
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16

beta_budget_ensemble_stat_significance %>% 
  ggplot(aes(abs(delta_median) / RSS, ttest_p_median, col = basin)) +
  geom_vline(xintercept = 1) +
  geom_hline(yintercept = 0.05) +
  geom_point() +
  scale_y_log10() +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(. ~ estimate)

Past versions of beta_budget_stats-2.png

Version	Author	Date
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18

beta_budget_ensemble_stat_significance %>%
  mutate(
    `1994 - 2004` = paste(`std_case 1994 - 2004`, `sd 1994 - 2004`, sep = " ± "),
    `2004 - 2014` = paste(`std_case 2004 - 2014`, `sd 2004 - 2014`, sep = " ± "),
    `Deacadal change` = paste0(delta_std_case, " ± ", RSS, " ", if_else(abs(delta_std_case) > RSS, "*", ""))
  ) %>%
  select(Region = basin,
         Esimate = estimate,
         `1994 - 2004`,
         `2004 - 2014`,
         `Deacadal change`) %>%
  write_csv(here::here("output/publication/Table_budget_stats.csv"))

beta_budget_basin_MLR_all_ensemble %>%
  filter(data_source == "obs",
         period != "1994 - 2014",
         !(Version_ID %in% Version_IDs)) %>%
  ggplot(aes(period, beta)) +
  geom_errorbar(
    data = tcant_budget_basin_MLR %>% 
      rename(beta = beta_1994),
    aes(x = period,
        ymax = beta, ymin = beta,
        col = "Scaled 1994\nestimate"),
    width = 0.5,
    linetype = 2,
    position = position_nudge(x = 0.1)
  ) +
  geom_jitter(
    data = beta_budget_basin_MLR_all_no_adj %>%
      filter(data_source == "obs",
             period != "1994 - 2014"),
    aes(fill = "Unadjusted data",
        alpha = "Unadjusted data"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_point(
    aes(fill = "Ensemble member",
        alpha = "Ensemble member"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_crossbar(
    data = beta_budget_ensemble_stat %>% 
      filter(estimate == "beta"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - 2*sd,
      ymax = std_case + 2*sd,
      linetype = "Ensemble SD"
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = beta_budget_ensemble_stat %>% 
      filter(estimate == "beta"),
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd,
      alpha = "Ensemble SD",
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = beta_budget_ensemble_stat %>% 
      filter(estimate == "beta"),
    aes(x = period,
        y = std_case,
        fill = "Standard case",
        alpha = "Standard case"),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  ) +
  scale_fill_manual(name = "group", values = c("#BB5566", "white", "#BBBBBB")) +
  scale_color_manual(values = c("grey50"), label = expression(Scaled~C[ant]~1994)) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.5,1,0.5)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = dcant_pgc_scaled_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank())

Past versions of beta_ensemble_scatter_plot-1.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
0c6db30	jens-daniel-mueller	2022-07-20
b18b250	jens-daniel-mueller	2022-07-20
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
d2ae54c	jens-daniel-mueller	2022-07-18

ggsave(path = here::here("output/publication"),
       filename = "FigS_beta_budgets_ensemble_member.png",
       height = 6,
       width = 8)

10.1.3 Biases

dcant_budget_basin_MLR_all_ensemble_bias <- 
dcant_budget_basin_MLR_all_ensemble %>%
  filter(data_source %in% c("mod", "mod_truth")) %>% 
  pivot_wider(names_from = data_source,
              values_from = dcant) %>% 
  mutate(dcant_bias = mod - mod_truth,
         dcant_bias_rel = 100 * dcant_bias / mod_truth)

dcant_budget_basin_MLR_all_ensemble_bias <- 
dcant_budget_basin_MLR_all_ensemble_bias %>%
  mutate(ensemble_role = if_else(Version_ID %in% Version_IDs,
                                 "Standard case",
                                 "others"))

dcant_budget_basin_MLR_all_ensemble_bias %>%
  ggplot(aes(period, dcant_bias)) +
  geom_hline(yintercept = 0) +
  geom_point(
    aes(fill = Version_ID_group,
        col = ensemble_role),
    size = 1.3,
    alpha = .7,
    shape = 21,
    position = position_dodge2(width = 0.3)
  ) +
  stat_summary(
    fun.data = "mean_sdl",
    fun.args = list(mult = 2),
    position = position_nudge(x = -0.3),
    col = "grey"
  ) +
  stat_summary(
    fun.data = "mean_sdl",
    fun.args = list(mult = 1),
    position = position_nudge(x = -0.3),
    size = 0.1
  ) +
  scale_fill_brewer(palette = "Dark2") +
    scale_color_manual(values = c("transparent", "black"),
                     name = "Ensemble member") +
  facet_grid(basin ~ .)

Past versions of budget_biases-1.png

Version	Author	Date
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09

dcant_budget_bias_ensemble_stat <-
  dcant_budget_basin_MLR_all_ensemble_bias %>%
  filter(period != "1994 - 2014") %>%
  group_by(period, basin) %>%
  summarise(
    median = median(dcant_bias),
    sd = sd(dcant_bias),
    n = n()
  ) %>%
  ungroup() 


dcant_budget_bias_ensemble_stat <-
  full_join(
    dcant_budget_bias_ensemble_stat,
    dcant_budget_basin_MLR_all_ensemble_bias %>%
        filter(Version_ID %in% Version_IDs,
               period != "1994 - 2014") %>%
        select(period,
               basin,
               std_case = dcant_bias)
  )




dcant_budget_basin_MLR_all_ensemble_bias %>%
  filter(period != "1994 - 2014") %>%
  ggplot(aes(period, dcant_bias)) +
  geom_hline(yintercept = 0) +
  geom_point(
    aes(fill = "Ensemble member",
        alpha = "Ensemble member"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  geom_crossbar(
    data = dcant_budget_bias_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      ymin = std_case - 2 * sd,
      ymax = std_case + 2 * sd,
      linetype = "Ensemble SD"
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = dcant_budget_bias_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      ymin = std_case - sd,
      ymax = std_case + sd,
      alpha = "Ensemble SD",
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = dcant_budget_bias_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      fill = "Standard case",
      alpha = "Standard case"
    ),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  )+
  scale_fill_manual(name = "group", values = c("#BB5566", "white", "#BBBBBB")) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.5,1,0.5)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = dcant_bias_pgc_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank())

Past versions of budget_biases-2.png

Version	Author	Date
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_bias_budgets_ensemble_member.png",
       height = 6,
       width = 8)



dcant_budget_basin_MLR_all_ensemble_bias %>% 
  ggplot(aes(period, dcant_bias_rel, col = Version_ID_group)) +
  geom_hline(yintercept = 0) +
  geom_jitter(alpha = 0.5) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ MLR_basins)

Past versions of budget_biases-3.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09

delta_dcant_budget_basin_MLR_all_ensemble <-
dcant_budget_basin_MLR_all_ensemble_bias %>%
  filter(period %in% two_decades) %>%
  select(basin, period, MLR_basins, Version_ID_group, mod, mod_truth, Version_ID) %>%
  pivot_longer(cols = mod:mod_truth,
               names_to = "data_source",
               values_to = "dcant") %>%
  group_by(basin, MLR_basins, Version_ID_group, data_source) %>%
  arrange(period) %>%
  mutate(delta_dcant = dcant - lag(dcant)) %>%
  ungroup() %>%
  drop_na() %>%
  select(-dcant) 

delta_dcant_budget_basin_MLR_all_ensemble_bias <- 
  delta_dcant_budget_basin_MLR_all_ensemble %>%
  pivot_wider(names_from = data_source,
              values_from = delta_dcant) %>%
  mutate(delta_dcant_bias = mod - mod_truth,
         delta_dcant_bias_rel = 100 * delta_dcant_bias / mod_truth)

delta_dcant_budget_basin_MLR_all_ensemble %>% 
  ggplot(aes(data_source, delta_dcant, col = Version_ID_group)) +
  geom_hline(yintercept = 0) +
  geom_jitter(alpha = 0.5) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ MLR_basins)

Past versions of budget_biases-4.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09

delta_dcant_budget_basin_MLR_all_ensemble_bias %>% 
  ggplot(aes(period, delta_dcant_bias, col = Version_ID_group)) +
  geom_hline(yintercept = 0) +
  geom_jitter(alpha = 0.5) +
  scale_color_brewer(palette = "Dark2") +
  facet_grid(basin ~ MLR_basins)

Past versions of budget_biases-5.png

Version	Author	Date
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09

delta_dcant_budget_bias_ensemble_stat <-
  delta_dcant_budget_basin_MLR_all_ensemble_bias %>%
  group_by(basin) %>%
  summarise(
    median = median(delta_dcant_bias),
    sd = sd(delta_dcant_bias),
    n = n()
  ) %>%
  ungroup() 


delta_dcant_budget_bias_ensemble_stat <-
  full_join(
    delta_dcant_budget_bias_ensemble_stat,
    delta_dcant_budget_basin_MLR_all_ensemble_bias %>%
        filter(Version_ID %in% Version_IDs,
               period != "1994 - 2014") %>%
        select(period,
               basin,
               std_case = delta_dcant_bias)
  )



delta_dcant_budget_bias_ensemble_stat_RSS <-
  dcant_budget_bias_ensemble_stat %>% 
  arrange(period) %>% 
  group_by(basin) %>% 
  mutate(delta_std_case = std_case - lag(std_case),
         RSS = sqrt(sd^2 + lag(sd)^2)) %>% 
  ungroup() %>% 
  drop_na()
  



delta_dcant_budget_basin_MLR_all_ensemble_bias %>%
  ggplot(aes(period, delta_dcant_bias)) +
  geom_hline(yintercept = 0) +
  geom_point(
    aes(fill = "Ensemble member",
        alpha = "Ensemble member"),
    shape = 21,
    position = position_jitter(width = 0.1, height = 0)
  ) +
  # geom_crossbar(
  #   data = delta_dcant_budget_bias_ensemble_stat,
  #   aes(
  #     x = period,
  #     y = std_case,
  #     ymin = std_case - 2 * sd,
  #     ymax = std_case + 2 * sd,
  #     linetype = "Ensemble SD"
  #   ),
  #   width = 0.1,
  #   fill = "#AA3377",
  #   alpha = 0.2,
  #   position = position_nudge(x = 0.2)
  # ) +
  # geom_crossbar(
  #   data = delta_dcant_budget_bias_ensemble_stat,
  #   aes(
  #     x = period,
  #     y = std_case,
  #     ymin = std_case - sd,
  #     ymax = std_case + sd,
  #     alpha = "Ensemble SD",
  #   ),
  #   width = 0.1,
  #   fill = "#AA3377",
  #   alpha = 0.4,
  #   position = position_nudge(x = 0.2)
  # ) +
  geom_crossbar(
    data = delta_dcant_budget_bias_ensemble_stat_RSS,
    aes(
      x = period,
      y = delta_std_case,
      ymin = delta_std_case - 2 * RSS,
      ymax = delta_std_case + 2 * RSS,
      linetype = "RSS"
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.2,
    position = position_nudge(x = 0.2)
  ) +
  geom_crossbar(
    data = delta_dcant_budget_bias_ensemble_stat_RSS,
    aes(
      x = period,
      y = delta_std_case,
      ymin = delta_std_case - RSS,
      ymax = delta_std_case + RSS,
      alpha = "RSS",
    ),
    width = 0.1,
    fill = "#AA3377",
    alpha = 0.4,
    position = position_nudge(x = 0.2)
  ) +
  geom_point(
    data = delta_dcant_budget_bias_ensemble_stat,
    aes(
      x = period,
      y = std_case,
      fill = "Standard case",
      alpha = "Standard case"
    ),
    size = 2,
    shape = 21,
    position = position_nudge(x = 0.2)
  )+
  scale_fill_manual(name = "group", values = c("#BB5566", "white", "#BBBBBB")) +
  scale_linetype(name = "X") +
  scale_alpha_manual(name = "group", values = c(0.5,1,0.5)) +
  scale_shape_manual(values = 95) +
  scale_y_continuous(name = delta_dcant_bias_pgc_label) +
  guides(
    fill = guide_legend(order = 1),
    linetype = guide_legend(order = 2),
    alpha = guide_legend(order = 1)
  ) +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_y") +
  theme(legend.title = element_blank(),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(), 
        panel.grid.major.x = element_blank())

Past versions of budget_biases-6.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09

ggsave(path = here::here("output/publication"),
       filename = "FigS_delta_dcant_bias_budgets_ensemble_member.png",
       height = 6,
       width = 6)

10.2 Flow chart

dcant_budget_basin_MLR_all <- 
  dcant_budget_basin_MLR_all %>% 
  filter(basin != "Global")

dcant_budget_basin_MLR_all <- left_join(dcant_budget_basin_MLR_all,
                           co2_atm %>% rename(tref1 = year,
                                              pCO21 = pCO2))

dcant_budget_basin_MLR_all <- left_join(dcant_budget_basin_MLR_all,
                           co2_atm %>% rename(tref2 = year,
                                              pCO22 = pCO2))


dcant_budget_basin_MLR_all_plot <- dcant_budget_basin_MLR_all %>%
  filter(period != "1994 - 2014",
         data_source == "obs")

tcant_budget_basin_MLR <-
  tcant_inv %>%
  mutate(method = "layer",
         data_source = "obs") %>% 
  group_by(basin) %>%
  nest() %>% 
  mutate(budget = map(.x = data, ~m_dcant_budget(.x))) %>% 
  select(-data) %>%
  unnest(budget)

tcant_budget_basin_MLR <-
  full_join(tcant_budget_basin_MLR,
            area_scaling %>% select(basin, scaling_factor)) %>%
  mutate(value = value * scaling_factor) %>%
  select(-scaling_factor)


tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  mutate(pCO21 = 281,
         tref1 = 1800,
         tref2 = 1994)

tcant_budget_basin_MLR <- left_join(tcant_budget_basin_MLR,
                                    co2_atm %>% rename(tref2 = year,
                                                       pCO22 = pCO2))

tcant_budget_basin_MLR <- tcant_budget_basin_MLR %>% 
  mutate(period = paste(tref1, tref2, sep = " - ")) %>% 
  filter(estimate == "dcant") %>% 
  select(-estimate) %>% 
  rename(dcant = value)

dcant_budget_basin_MLR_all_plot <- bind_rows(
  dcant_budget_basin_MLR_all_plot,
  tcant_budget_basin_MLR)

dcant_budget_basin_MLR_all_plot <-
  left_join(dcant_budget_basin_MLR_all_plot,
            dcant_budget_basin_MLR_all_ensemble_summary)

10.2.1 Absolute

g1 <- dcant_budget_basin_MLR_all_plot %>%
  filter(period != "1800 - 1994") %>% 
  ggplot(aes(
    y = dcant,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin
  )) +
  stat_alluvium(decreasing = FALSE) +
  stat_stratum(decreasing = FALSE) +
  stat_stratum(geom = "text",
               decreasing = FALSE,
               aes(label = paste(
                 round(after_stat(max-min),1)
                 # 100*round(after_stat(prop), 2), "%"
                 ))) +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin, label = basin),
    decreasing = FALSE
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 32), expand = c(0, 0)) +
  labs(y = dcant_pgc_label) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank()) +
  theme_classic()


newdat <- tibble(layer_data(g1))

change <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  pivot_wider(names_from = x,
              values_from = count) %>% 
  mutate(dcant_change = round(100*(`2` - `1`) / `1`)) %>% 
  select(alluvium, dcant_change)

coord <- newdat %>%
  filter(x == 2) %>% 
  select(x, y, alluvium)

new_layer <- full_join(
  change,
  coord
)

new_layer <- new_layer %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste(dcant_change, "%"))

g1 +
  geom_text(data = new_layer,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change
            ),
            inherit.aes = FALSE)

Past versions of budgets_basins_hemisphere_alluvial_order_budget-1.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
d2ae54c	jens-daniel-mueller	2022-07-18
d20faeb	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16
6be73e0	jens-daniel-mueller	2022-06-30
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

g2 <- dcant_budget_basin_MLR_all_plot %>%
    filter(period != "1800 - 1994") %>%
  ggplot(aes(
    y = dcant,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin,
    label = basin
  )) +
  geom_alluvium() +
  geom_stratum() +
  stat_stratum(geom = "text",
               aes(label = paste(
                 round(after_stat(count),1)
                 # 100*round(after_stat(prop), 2), "%"
                 ))) +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin)
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_color_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 33), expand = c(0, 0)) +
  guides(y = "none") +
  labs(title = dcant_pgc_label) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5))


newdat <- tibble(layer_data(g2))

change_basin <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  pivot_wider(names_from = x,
              values_from = count) %>% 
  mutate(dcant_change = round(100*(`2` - `1`) / `1`)) %>% 
  select(alluvium, dcant_change)

coord_basin <- newdat %>%
  filter(x == 2) %>% 
  select(x, y, alluvium)

new_layer_basin <- full_join(
  change_basin,
  coord_basin
)

new_layer_basin <- new_layer_basin %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste(dcant_change, "%"))

new_layer_total <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(x) %>% 
  summarise(dcant_change = sum(count)) %>% 
  ungroup()

new_layer_total <- new_layer_total %>% 
  mutate(y = dcant_change,
         dcant_change = as.character(round(dcant_change,1)),
         dcant_change = paste("global:",dcant_change))

g2 +
  geom_text(data = new_layer_basin,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change
            ),
            inherit.aes = FALSE) +
  geom_label(data = new_layer_total,
            aes(
              x = x,
              y = y + 1,
              label = dcant_change
            ),
            inherit.aes = FALSE)

Past versions of budgets_basins_hemisphere_alluvial_order_basin-1.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16
6be73e0	jens-daniel-mueller	2022-06-30
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
ec7fe7e	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27
9753eb8	jens-daniel-mueller	2022-01-26
b1d7720	jens-daniel-mueller	2022-01-21
4fe7150	jens-daniel-mueller	2022-01-21

10.2.2 Scaled

dcant_budget_basin_MLR_all_plot <- left_join(dcant_budget_basin_MLR_all_plot,
                           co2_atm %>% rename(tref1 = year,
                                              pCO21 = pCO2))

dcant_budget_basin_MLR_all_plot <- left_join(dcant_budget_basin_MLR_all_plot,
                           co2_atm %>% rename(tref2 = year,
                                              pCO22 = pCO2))

dcant_budget_basin_MLR_all_plot <- dcant_budget_basin_MLR_all_plot %>% 
  mutate(delta_pCO2 = pCO22 - pCO21,
         beta = dcant / delta_pCO2,
         dcant_sd_scaled = dcant_sd / delta_pCO2) %>% 
  select(-starts_with("pCO2"))


dcant_budget_global_all_ensemble_summary <- left_join(dcant_budget_global_all_ensemble_summary,
                           co2_atm %>% rename(tref1 = year,
                                              pCO21 = pCO2))

dcant_budget_global_all_ensemble_summary <- left_join(dcant_budget_global_all_ensemble_summary,
                           co2_atm %>% rename(tref2 = year,
                                              pCO22 = pCO2))

dcant_budget_global_all_ensemble_summary <- dcant_budget_global_all_ensemble_summary %>% 
  mutate(delta_pCO2 = pCO22 - pCO21,
         beta = dcant_mean / delta_pCO2,
         dcant_sd_scaled = dcant_sd / delta_pCO2) %>% 
  select(-starts_with("pCO2"))

g2 <- dcant_budget_basin_MLR_all_plot %>%
  filter(period != "1800 - 1994") %>% 
  ggplot(aes(
    y = beta,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin,
    label = basin
  )) +
  geom_alluvium() +
  geom_stratum() +
  stat_stratum(geom = "text",
               aes(label = paste(
                 round(after_stat(count),2)
                 # 100*round(after_stat(prop), 2), "%"
                 ))) +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin)
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_color_brewer(palette = "Paired", guide = "none") +
  # scale_y_continuous(limits = c(0, 33), expand = c(0, 0)) +
  guides(y = "none") +
  labs(title = dcant_pgc_scaled_label) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5))


newdat <- tibble(layer_data(g2))

change_basin <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  pivot_wider(names_from = x,
              values_from = count) %>% 
  mutate(dcant_change = round(100*(`2` - `1`) / `1`)) %>% 
  select(alluvium, dcant_change)

coord_basin <- newdat %>%
  filter(x == 2) %>% 
  select(x, y, alluvium)

new_layer_basin <- full_join(
  change_basin,
  coord_basin
)

new_layer_basin <- new_layer_basin %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste(dcant_change, "%"))

new_layer_total <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(x) %>% 
  summarise(dcant_change = sum(count)) %>% 
  ungroup()

new_layer_total <- new_layer_total %>% 
  mutate(y = dcant_change,
         dcant_change = as.character(round(dcant_change,2)),
         dcant_change = paste("global:",dcant_change))

g2 +
  geom_text(data = new_layer_basin,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change
            ),
            inherit.aes = FALSE) +
  geom_label(data = new_layer_total,
            aes(
              x = x,
              y = y + 0.05,
              label = dcant_change
            ),
            inherit.aes = FALSE)

Past versions of budgets_basins_hemisphere_alluvial_order_basin_scaled-1.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16
6be73e0	jens-daniel-mueller	2022-06-30
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
2116dd3	jens-daniel-mueller	2022-02-09
d2191ad	jens-daniel-mueller	2022-02-04
ed903f7	jens-daniel-mueller	2022-02-02

g2 <- dcant_budget_basin_MLR_all_plot %>%
  ggplot(aes(
    y = beta,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin,
    label = basin
  )) +
  geom_alluvium() +
  geom_stratum() +
  stat_stratum(geom = "text",
               aes(label = paste(
                 round(after_stat(count),2)
                 # 100*round(after_stat(prop), 2), "%"
                 ))) +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin)
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_color_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 1.7), expand = c(0, 0)) +
  guides(y = "none") +
  labs(title = dcant_pgc_scaled_label) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5))


newdat <- tibble(layer_data(g2))

change_basin <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(alluvium) %>% 
  arrange(x) %>% 
  mutate(dcant_change = round(100*(count - lag(count)) / lag(count))) %>% 
  ungroup()

coord_basin <- newdat %>%
  select(x, y, alluvium)

new_layer_basin <- left_join(
  change_basin %>% drop_na(),
  coord_basin
)

new_layer_basin <- new_layer_basin %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste(dcant_change, "%"))

new_layer_total <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(x) %>% 
  summarise(dcant_change = sum(count)) %>% 
  ungroup()

new_layer_total <- new_layer_total %>% 
  mutate(y = dcant_change,
         dcant_change = as.character(round(dcant_change,2)),
         dcant_change = paste("global:",dcant_change))

g2 +
  geom_text(data = new_layer_basin,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change
            ),
            inherit.aes = FALSE) +
  geom_label(data = new_layer_total,
            aes(
              x = x,
              y = y + 0.1,
              label = dcant_change
            ),
            inherit.aes = FALSE)

Past versions of budgets_basins_hemisphere_alluvial_order_basin_scaled_incl_sabine-1.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16
6be73e0	jens-daniel-mueller	2022-06-30
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
b599680	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
2116dd3	jens-daniel-mueller	2022-02-09
d2191ad	jens-daniel-mueller	2022-02-04
fa46251	jens-daniel-mueller	2022-02-02
7655085	jens-daniel-mueller	2022-02-02
226d67d	jens-daniel-mueller	2022-02-02

g2 <- dcant_budget_basin_MLR_all_plot %>%
  ggplot(aes(
    y = beta,
    x = period,
    alluvium = basin,
    fill  = basin,
    stratum = basin,
    label = basin
  )) +
  geom_alluvium() +
  geom_stratum() +
  ggrepel::geom_label_repel(
    data = dcant_budget_basin_MLR_all_plot %>% filter(period == "2004 - 2014"),
    stat = "stratum",
    size = 4,
    nudge_x = .5,
    point.padding = 3,
    aes(fill = basin)
  )+
  scale_fill_brewer(palette = "Paired", guide = "none") +
  scale_color_brewer(palette = "Paired", guide = "none") +
  scale_y_continuous(limits = c(0, 1.6), expand = c(0, 0)) +
  guides(y = "none") +
  labs(title = dcant_pgc_scaled_label) +
  theme_classic() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),
        axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        plot.title = element_text(hjust = 0.5))


newdat <- tibble(layer_data(g2))

# construct budget labels

budget_basin <- 
  newdat %>% 
  select(x, y, alluvium, count)

budget_layer_basin <- budget_basin %>% 
  mutate(dcant = as.character(round(count,2)))

# construct uncertainty labels

uncertainty_basin <- 
  dcant_budget_basin_MLR_all_plot %>% 
  mutate(x = fct_recode(period,
                        "2" = "1994 - 2004",
                        "3" = "2004 - 2014")) %>% 
  select(x, alluvium = basin, dcant_sd_scaled) %>% 
  drop_na() %>% 
  arrange(x, alluvium)
  

coord_basin <- newdat %>%
  select(x, y, alluvium) %>% 
  filter(x != 1)

uncertainty_layer_basin <- bind_cols(
  coord_basin,
  uncertainty_basin %>% select(dcant_sd_scaled),
)

# construct change labels

change_basin <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(alluvium) %>% 
  arrange(x) %>% 
  mutate(dcant_change_abs = count - lag(count),
         dcant_change = round(100*(dcant_change_abs) / lag(count))) %>% 
  ungroup()

coord_basin <- newdat %>%
  select(x, y, alluvium)

new_layer_basin <- left_join(
  change_basin %>% drop_na(),
  coord_basin
)

new_layer_basin <- new_layer_basin %>% 
  mutate(dcant_change = as.character(dcant_change),
         dcant_change = if_else(str_detect(dcant_change, "-"),
                                dcant_change,
                                paste0("+", dcant_change)),
         dcant_change = paste0(dcant_change, "%"))

# construct global budget labels

new_layer_total <- 
  newdat %>% 
  select(x, alluvium, count) %>% 
  group_by(x) %>% 
  summarise(dcant_change = sum(count)) %>% 
  ungroup()

dcant_sd_scaled <- round(c(
  0.24,
  dcant_budget_global_all_ensemble_summary %>%
    pull(dcant_sd_scaled)
), 2)

new_layer_total <- bind_cols(new_layer_total, dcant_sd_scaled = dcant_sd_scaled)

new_layer_total <- new_layer_total %>% 
  mutate(y = dcant_change,
         dcant_sd_scaled_rel = round(100 * dcant_sd_scaled/dcant_change),
         dcant_change = as.character(round(dcant_change,2)),
         dcant_sd_scaled = round(dcant_sd_scaled,1),
         dcant_change = paste0(dcant_change, "\n(±", dcant_sd_scaled, ")"))

# basin change + uncertainty

budget_layer_basin <- full_join(budget_layer_basin,
                                uncertainty_layer_basin)

budget_layer_basin <- 
  budget_layer_basin %>% 
  mutate(dcant_sd_scaled_rel = 100*dcant_sd_scaled/count,
         dcant_sd_scaled_rel = as.character(round(dcant_sd_scaled_rel)),
         dcant_sd_scaled_numeric = dcant_sd_scaled,
         dcant_sd_scaled = round(dcant_sd_scaled, 2),
         dcant_sd_scaled = paste0("(±", dcant_sd_scaled, ")"),
         dcant_label = if_else(dcant_sd_scaled != "(±NA)",
                               paste0(dcant, "\n", dcant_sd_scaled),
                               dcant))


# uncertainty changes

uncertainty_changes <- budget_layer_basin %>% 
  select(x, alluvium, dcant_sd_scaled = dcant_sd_scaled_numeric) %>% 
  group_by(alluvium) %>% 
  mutate(dcant_change_uncert = sqrt(dcant_sd_scaled^2 + lag(dcant_sd_scaled)^2)) %>% 
  ungroup()


new_layer_basin <- left_join(new_layer_basin, uncertainty_changes) %>% 
  mutate(dcant_change_uncert_rel = 100*dcant_change_uncert/abs((count + lag(count))/2),
         dcant_change_label = if_else(!is.na(dcant_change_uncert),
                                      paste0(dcant_change, "\n(±", round(dcant_change_uncert_rel), "%)"),
                                      dcant_change))


g2 +
  geom_text(data = budget_layer_basin,
            aes(
              x = x,
              y = y,
              label = dcant_label
            ),
            size = 3,
            inherit.aes = FALSE) +
  geom_text(data = new_layer_basin,
            aes(
              x = x - 0.3,
              y = y,
              label = dcant_change_label
            ),
            size = 4,
            inherit.aes = FALSE)

Past versions of budgets_basins_hemisphere_alluvial_order_basin_scaled_incl_sabine_uncertainty-1.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
efa414b	jens-daniel-mueller	2022-07-16
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
6be73e0	jens-daniel-mueller	2022-06-30
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
a26a21d	jens-daniel-mueller	2022-06-27
748aa43	jens-daniel-mueller	2022-06-27
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
8dca96a	jens-daniel-mueller	2022-04-12
209c9b6	jens-daniel-mueller	2022-04-10
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
b599680	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
094bfa0	jens-daniel-mueller	2022-02-18
2116dd3	jens-daniel-mueller	2022-02-09
a6b33aa	jens-daniel-mueller	2022-02-04
4b48475	jens-daniel-mueller	2022-02-04
fec5a1e	jens-daniel-mueller	2022-02-04
d2191ad	jens-daniel-mueller	2022-02-04
c7b4984	jens-daniel-mueller	2022-02-02
7fb28a2	jens-daniel-mueller	2022-02-02
49097e8	jens-daniel-mueller	2022-02-02
fe11bfd	jens-daniel-mueller	2022-02-02

  # geom_label(data = new_layer_total,
  #           aes(
  #             x = x,
  #             y = y + 0.13,
  #             label = dcant_change
  #           ),
  #           size = 4,
  #           inherit.aes = FALSE)



# ggsave(path = here::here("output/publication"),
#        filename = "Fig_budget_beta_basin_hemisphere.png",
#        height = 6,
#        width = 8)

10.3 Depth layer

10.3.1 Global

dcant_budget_change <- dcant_budget_global_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  select(-c(tref1, tref2, Version_ID)) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(sign = if_else(`2004 - 2014` - `1994 - 2004` > 0,
                        "increase",
                        "decrease"))

dcant_budget_layer <- dcant_budget_global_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  group_by(basin, period, data_source) %>% 
  mutate(dcant = if_else(is.na(lead(dcant)), 888, dcant),
         dcant = na_if(dcant, 888)) %>%
  fill(dcant) %>% 
  ungroup()

dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_rect(
    data = dcant_budget_change,
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = `1994 - 2004`,
      ymax = `2004 - 2014`,
      fill = sign
    ),
    alpha = 0.4
  ) +
  geom_step(aes(inv_depth - 250, dcant, col = period), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500)) +
  scale_color_brewer(palette = "Set1", direction = -1, name = "period") +
  scale_fill_brewer(palette = "Set1", direction = -1, name = "") +
  facet_wrap(~ basin, ncol = 3, dir = "v") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)") +
      theme(legend.position = c(0.8,0.2))

Past versions of depth_layer_budget_global_standard_case-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
17cd1d1	jens-daniel-mueller	2022-07-13

dcant_budget_change <- dcant_budget_global_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  arrange(inv_depth) %>% 
  group_by(data_source, basin, period) %>% 
  mutate(dcant_cum = cumsum(dcant)) %>% 
  ungroup() %>%
  select(-c(tref2, Version_ID, dcant)) %>%
  pivot_wider(names_from = period,
              values_from = dcant_cum) %>%
  mutate(sign = if_else(`2004 - 2014` - `1994 - 2004` > 0,
                        "increase",
                        "decrease"))

dcant_budget_layer <- dcant_budget_global_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  arrange(inv_depth) %>% 
  group_by(data_source, basin, period) %>% 
  mutate(dcant_cum = cumsum(dcant)) %>% 
  ungroup() %>%
  group_by(basin, period, data_source) %>% 
  mutate(dcant_cum = if_else(is.na(lead(dcant_cum)), 888, dcant_cum),
         dcant_cum = na_if(dcant_cum, 888)) %>%
  fill(dcant_cum) %>% 
  ungroup()



dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_rect(
    data = dcant_budget_change,
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = `1994 - 2004`,
      ymax = `2004 - 2014`,
      fill = sign
    ),
    alpha = 0.3
  ) +
  geom_step(aes(inv_depth - 250, dcant_cum, col = period), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500)) +
  scale_color_brewer(palette = "Set1",
                     direction = -1,
                     name = "Decade") +
  scale_fill_brewer(palette = "Set1",
                    direction = -1,
                    name = "Decadal change") +
  
  facet_wrap( ~ basin, ncol = 3, dir = "v") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)",
       title = "Cumulative layer inventory change") +
      theme(legend.position = c(0.8,0.2))

Past versions of depth_layer_budget_global_cumulative-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
17cd1d1	jens-daniel-mueller	2022-07-13

10.3.2 5 basins

dcant_budget_change <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  select(-c(tref1, tref2, Version_ID)) %>%
  pivot_wider(names_from = period,
              values_from = dcant) %>%
  mutate(sign = if_else(`2004 - 2014` - `1994 - 2004` > 0,
                        "increase",
                        "decrease"))

dcant_budget_layer <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  group_by(basin, period, data_source) %>% 
  mutate(dcant = if_else(is.na(lead(dcant)), 888, dcant),
         dcant = na_if(dcant, 888)) %>%
  fill(dcant) %>% 
  ungroup()

dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_rect(
    data = dcant_budget_change,
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = `1994 - 2004`,
      ymax = `2004 - 2014`,
      fill = sign
    ),
    alpha = 0.4
  ) +
  geom_step(aes(inv_depth - 250, dcant, col = period), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500)) +
  scale_color_brewer(palette = "Set1", direction = -1, name = "period") +
  scale_fill_brewer(palette = "Set1", direction = -1, name = "") +
  facet_wrap(~ basin, ncol = 3, dir = "v") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)") +
      theme(legend.position = c(0.8,0.2))

Past versions of depth_layer_budget_basin_MLR_standard_case-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
26e9496	jens-daniel-mueller	2022-07-12

dcant_budget_range <- dcant_budget_basin_MLR_layer_all_ensemble %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  select(-c(tref1, tref2, Version_ID)) %>%
  group_by(basin, inv_depth, period) %>% 
  summarise(dcant_sd = sd(dcant)*2) %>%
  ungroup()

dcant_budget_layer <-
  full_join(dcant_budget_range,
            dcant_budget_layer)

p_dcant_budget_depth_layer <- 
dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0, size = 0.1) +
  geom_vline(xintercept = 1000, size = 0.1) +
  geom_rect(
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = dcant - dcant_sd,
      ymax = dcant + dcant_sd,
      fill = period
    ),
    alpha = 0.4
  ) +
  geom_step(aes(inv_depth - 250, dcant, col = period), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500), limits = c(3000, 0)) +
  scale_color_manual(values = c("#EE7733", "#009988"), name = "Mean \u00B1 2 SD") +
  scale_fill_manual(values = c("#EE7733", "#009988"), name = "Mean \u00B1 2 SD") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)") +
  facet_wrap(~ basin, ncol = 3, dir = "v", scales = "free_x") +
  theme(legend.position = c(0.9, 0.1))

p_dcant_budget_depth_layer

Past versions of depth_layer_budget_basin_MLR_ensemble-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
26e9496	jens-daniel-mueller	2022-07-12

p_dcant_profiles + p_dcant_budget_depth_layer +
  plot_layout(ncol = 1) +
  plot_annotation(tag_levels = 'A')

Past versions of depth_layer_budget_basin_MLR_ensemble-2.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_profiles_and_depth_layer_budget.png",
       height = 12,
       width = 8)

dcant_budget_layer <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source != "obs") %>%
  rename(dcant = value) %>%
  group_by(basin, period, data_source) %>% 
  mutate(dcant = if_else(is.na(lead(dcant)), 888, dcant),
         dcant = na_if(dcant, 888)) %>%
  fill(dcant) %>% 
  ungroup()

dcant_budget_range <- dcant_budget_basin_MLR_layer_all_ensemble %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source != "obs") %>%
  rename(dcant = value) %>%
  select(-c(tref1, tref2, Version_ID)) %>%
  group_by(basin, inv_depth, period, data_source) %>% 
  summarise(dcant_sd = sd(dcant)*2) %>%
  ungroup()

dcant_budget_layer <-
  full_join(dcant_budget_range,
            dcant_budget_layer)

p_dcant_budget_depth_layer_mod <- 
dcant_budget_layer %>%
      mutate(data_source = case_when(
    data_source == "mod" ~ "Reconstruction",
    data_source == "mod_truth" ~ "Model truth"
  )) %>%  
  ggplot() +
  geom_hline(yintercept = 0, size = 0.1) +
  geom_vline(xintercept = 1000, size = 0.1) +
  geom_rect(
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = dcant - dcant_sd,
      ymax = dcant + dcant_sd,
      fill = data_source
    ),
    alpha = 0.4
  ) +
  geom_step(aes(inv_depth - 250, dcant, col = data_source), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500), limits = c(3000, 0)) +
  scale_color_manual(values = c("#009988", "#EE7733"), name = "Mean \u00B1 2 SD") +
  scale_fill_manual(values = c("#009988", "#EE7733"), name = "Mean \u00B1 2 SD") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)") +
  facet_grid(period ~ basin, scales = "free_x") +
  theme(legend.position = "none")

p_dcant_budget_depth_layer_mod

Past versions of depth_layer_budget_basin_MLR_ensemble_mod-1.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11

p_dcant_profiles_mod + p_dcant_budget_depth_layer_mod +
  plot_layout(ncol = 1) +
  plot_annotation(tag_levels = 'A')

Past versions of depth_layer_budget_basin_MLR_ensemble_mod-2.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_profiles_and_depth_layer_budget_synthetic_data.png",
       height = 10,
       width = 7)

dcant_budget_basin_MLR_layer_all_ensemble_stat <-
dcant_budget_basin_MLR_layer_all_ensemble %>%
  filter(estimate == "dcant",
         data_source == "obs",
         inv_depth < 3000,
         period %in% two_decades) %>% 
  group_by(basin,
           period,
           inv_depth) %>%
  summarise(dcant_sd = sd(value) * 2) %>%
  ungroup()

dcant_layer_budget_stat <-
  full_join(
    dcant_budget_basin_MLR_layer_all %>%
      filter(
        estimate == "dcant",
        data_source == "obs",
        inv_depth < 3000,
        period %in% two_decades
      ) %>% 
      select(basin, period, inv_depth, dcant = value),
    dcant_budget_basin_MLR_layer_all_ensemble_stat
  )  

rm(dcant_budget_basin_MLR_layer_all_ensemble_stat)  

dcant_layer_budget_stat %>%
  filter(period %in% two_decades) %>%
  arrange(period) %>% 
  group_by(basin, inv_depth) %>%
  mutate(delta_dcant = dcant - lag(dcant),
         RSS = sqrt(dcant_sd ^ 2 + lag(dcant_sd ^ 2))) %>%
  ungroup() %>%
  drop_na() %>% 
  filter(abs(delta_dcant) >= RSS) %>% 
  select(basin, inv_depth, delta_dcant, RSS) %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

basin	inv_depth	delta_dcant	RSS
Indian	1750	-0.401	0.3518036
N. Atlantic	1750	-0.581	0.2469002
N. Atlantic	2250	-0.291	0.2060254
N. Atlantic	2750	-0.325	0.2312763
S. Atlantic	250	0.475	0.3500627
S. Atlantic	750	0.553	0.4456077
S. Atlantic	1250	0.553	0.3265119
S. Atlantic	1750	0.262	0.2554222

dcant_budget_change <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3000,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  arrange(inv_depth) %>% 
  group_by(data_source, basin, period) %>% 
  mutate(dcant_cum = cumsum(dcant)) %>% 
  ungroup() %>%
  select(-c(tref2, Version_ID, dcant)) %>%
  pivot_wider(names_from = period,
              values_from = dcant_cum) %>%
  mutate(sign = if_else(`2004 - 2014` - `1994 - 2004` > 0,
                        "increase",
                        "decrease"))

dcant_budget_layer <- dcant_budget_basin_MLR_layer_all %>%
  filter(estimate == "dcant",
         period != "1994 - 2014",
         inv_depth <= 3400,
         data_source == "obs") %>%
  rename(dcant = value) %>%
  arrange(inv_depth) %>% 
  group_by(data_source, basin, period) %>% 
  mutate(dcant_cum = cumsum(dcant)) %>% 
  ungroup() %>%
  group_by(basin, period, data_source) %>% 
  mutate(dcant_cum = if_else(is.na(lead(dcant_cum)), 888, dcant_cum),
         dcant_cum = na_if(dcant_cum, 888)) %>%
  fill(dcant_cum) %>% 
  ungroup()



dcant_budget_layer %>%
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_rect(
    data = dcant_budget_change,
    aes(
      xmin = inv_depth - 250,
      xmax = inv_depth + 250,
      ymin = `1994 - 2004`,
      ymax = `2004 - 2014`,
      fill = sign
    ),
    alpha = 0.3
  ) +
  geom_step(aes(inv_depth - 250, dcant_cum, col = period), direction = "vh",
            size = 1) +
  coord_flip() +
  scale_x_reverse(breaks = seq(0, 3000, 500)) +
  scale_color_brewer(palette = "Set1",
                     direction = -1,
                     name = "Decade") +
  scale_fill_brewer(palette = "Set1",
                    direction = -1,
                    name = "Decadal change") +
  
  facet_wrap( ~ basin, ncol = 3, dir = "v") +
  labs(y = dcant_layer_budget_label,
       x = "Depth (m)",
       title = "Cumulative layer inventory change") +
      theme(legend.position = c(0.8,0.2))

Past versions of depth_layer_budget_basin_MLR_cumulative-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
26e9496	jens-daniel-mueller	2022-07-12
37f56b3	jens-daniel-mueller	2022-07-01
df21d31	jens-daniel-mueller	2022-07-01
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
d2191ad	jens-daniel-mueller	2022-02-04
32e9682	jens-daniel-mueller	2022-02-02
913e42f	jens-daniel-mueller	2022-02-01
189de95	jens-daniel-mueller	2022-02-01
ab001eb	jens-daniel-mueller	2022-01-31
b62308d	jens-daniel-mueller	2022-01-31
5f2aed0	jens-daniel-mueller	2022-01-27

11 Time series

11.1 eMLR only

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs",
         period != "1994 - 2014") %>% 
  ggplot(aes(tref2, dcant, group = interaction(MLR_basins, Version_ID_group),
             col=Version_ID_group)) +
  geom_path() +
  geom_point()

Past versions of dcant_budget_time_series_member_connections-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs") %>% 
  select(MLR_basins, Version_ID_group, period, dcant) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  ggplot(aes(`1994 - 2004`, `2004 - 2014`,
             col=MLR_basins)) +
  coord_equal() +
  geom_point() +
  facet_wrap(~ Version_ID_group)

Past versions of dcant_budget_time_series_member_connections-2.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs") %>% 
  select(MLR_basins, Version_ID_group, period, dcant) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  ggplot(aes(`1994 - 2004`, `2004 - 2014`,
             col=Version_ID_group)) +
  coord_equal() +
  geom_point() +
  facet_wrap(~ MLR_basins)

Past versions of dcant_budget_time_series_member_connections-3.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs") %>% 
  select(MLR_basins, Version_ID_group, period, dcant) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  ggplot(aes(`1994 - 2004`, `1994 - 2014`,
             col=Version_ID_group)) +
  coord_equal() +
  geom_point()

Past versions of dcant_budget_time_series_member_connections-4.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
232909e	jens-daniel-mueller	2022-07-01

dcant_budget_global_all_ensemble %>% 
  filter(data_source == "obs") %>% 
  select(MLR_basins, Version_ID_group, period, dcant) %>% 
  pivot_wider(names_from = period,
              values_from = dcant) %>% 
  ggplot(aes(`1994 - 2004` + `2004 - 2014`, `1994 - 2014`,
             col=Version_ID_group)) +
  geom_abline(intercept = 0, slope = 1) +
  coord_equal() +
  scale_x_continuous(breaks = seq(0,100,1)) +
  scale_y_continuous(breaks = seq(0,100,1)) +
  geom_point()

Past versions of dcant_budget_time_series_member_connections-5.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20
b1f7ab3	jens-daniel-mueller	2022-07-18

dcant_budget_global_ts <- dcant_budget_global_all %>% 
  filter(data_source == "obs",
         period  %in% two_decades) %>% 
  select(year = tref2, dcant_mean = dcant)


dcant_budget_global_all_ensemble_summary <-
  dcant_budget_basin_MLR_all_ensemble %>%
  filter(period %in% c("1994 - 2004", "1994 - 2014"),
         data_source == "obs",
         basin == "Global") %>%
  group_by(data_source, tref2, period) %>%
  summarise(#dcant_mean = mean(dcant),
            dcant_sd = sd(dcant)*2) %>%
  ungroup()

dcant_budget_global_ts <- left_join(dcant_budget_global_ts,
                                    dcant_budget_global_all_ensemble_summary %>% 
                                      select(year = tref2, dcant_sd))


tcant_S04 <- bind_cols(year = 1994, dcant_mean = 118, dcant_sd = 19)

tcant_ts <- full_join(dcant_budget_global_ts, tcant_S04)

tcant_ts <- left_join(tcant_ts, co2_atm)

co2_atm_pi <- bind_cols(pCO2 = 280, dcant_mean = 0, year = 1800, dcant_sd = NA)

tcant_ts <- full_join(tcant_ts, co2_atm_pi)


tcant_ts <- tcant_ts %>%
  arrange(year) %>%
  mutate(
    tcant = cumsum(dcant_mean),
    tcant_sd = sqrt(dcant_sd ^ 2 + nth(dcant_sd,2) ^ 2)
  ) %>%
  mutate(
    tcant_sd = case_when(
      year == 1800 ~ 0,
      year == 1994 ~ dcant_sd,
      TRUE ~ tcant_sd
    ),
    dcant_sd_int = dcant_sd,
    dcant_sd = case_when(
      year == 1994 ~ 0,
      TRUE ~ dcant_sd
    )
  )

beta <- 1.57
co2_atm_pi_value <- 283
atm_co2_col <- "#009988"
tcant_col <- "black"

p_tcant_year <- tcant_ts %>%
  ggplot(aes(year, tcant)) +
  geom_line(data = co2_atm_reccap2 %>% filter(year > 1790),
            aes(year, (pCO2 - co2_atm_pi_value) * beta),
            col = atm_co2_col) +
  geom_linerange(aes(
    ymin = tcant - dcant_sd_int,
    ymax = tcant + dcant_sd_int,
    col = "Estimate\n±\ninterval\nuncertainty"
  )) +
  geom_point(fill = "white", shape = 21, aes(col = "Estimate\n±\ninterval\nuncertainty")) +
  # geom_text(aes(label = year), nudge_x = -13, nudge_y = 6) +
  scale_y_continuous(name = expression(Total ~ oceanic ~ C[ant] ~ (PgC)),
                     sec.axis = sec_axis( ~ (. / beta + co2_atm_pi_value),
                                          name = expression(Atm. ~ pCO[2] ~ (µatm)))) +
  scale_x_continuous(name = "Year", breaks = seq(1800, 2000, 50)) +
  scale_color_manual(values = tcant_col) +
  theme(
    axis.title.y.right = element_text(color = atm_co2_col),
    axis.text.y.right = element_text(color = atm_co2_col),
    axis.ticks.y.right = element_line(color = atm_co2_col), 
    legend.background = element_rect(fill = "transparent"),
    legend.position = c(0.85, 0.2),
    legend.title = element_blank()
  )

p_tcant_year

Past versions of mean_tcant_over_time-1.png

Version	Author	Date
9bbc6a4	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
0825298	jens-daniel-mueller	2022-06-28
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
8dca96a	jens-daniel-mueller	2022-04-12
209c9b6	jens-daniel-mueller	2022-04-10
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
5a6be34	jens-daniel-mueller	2022-03-22
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
251c7cf	jens-daniel-mueller	2022-02-17
565224d	jens-daniel-mueller	2022-02-17
2116dd3	jens-daniel-mueller	2022-02-09
6fe70a1	jens-daniel-mueller	2022-02-05
a6b33aa	jens-daniel-mueller	2022-02-04

p_tcant_atm_co2 <- tcant_ts %>%
  ggplot(aes(pCO2, tcant)) +
  geom_ribbon(aes(
    ymin = tcant - tcant_sd,
    ymax = tcant + tcant_sd,
    fill = "1800 - 2014"
  ),
  alpha = 0.5) +
  geom_ribbon(aes(
    ymin = tcant - dcant_sd,
    ymax = tcant + dcant_sd,
    fill = "1994 - 2014"
  ),
  alpha = 0.5) +
  geom_line() +
  geom_point(shape = 21, fill = "white") +
  scale_x_continuous(breaks = seq(280, 400, 30)) +
  scale_fill_manual(values = c("grey60", "grey30"), name = "Cumulative\nuncertainty") +
  scale_color_manual(values = "black", name = "") +
  geom_text(aes(label = year), nudge_x = -6, nudge_y = 6) +
  labs(x = expression(Atmospheric ~ pCO[2] ~ (µatm)),
       y = expression(Total ~ oceanic ~ C[ant] ~ (PgC))) +
  theme(
    legend.background = element_rect(fill = "transparent"),
    legend.position = c(0.85, 0.25),
    axis.title.x = element_text(color = atm_co2_col),
    axis.text.x = element_text(color = atm_co2_col),
    axis.ticks.x = element_line(color = atm_co2_col)
  )

p_tcant_year / p_tcant_atm_co2 + 
  plot_annotation(tag_levels = 'A')

Past versions of mean_tcant_over_atm_co2-1.png

Version	Author	Date
9bbc6a4	jens-daniel-mueller	2022-07-20
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
0825298	jens-daniel-mueller	2022-06-28
87e9eb8	jens-daniel-mueller	2022-06-27
b52b159	jens-daniel-mueller	2022-06-27
1d73ec9	jens-daniel-mueller	2022-05-16
2ca0109	jens-daniel-mueller	2022-05-02
b018a9a	jens-daniel-mueller	2022-04-29
e09320d	jens-daniel-mueller	2022-04-12
8dca96a	jens-daniel-mueller	2022-04-12
209c9b6	jens-daniel-mueller	2022-04-10
acad2e2	jens-daniel-mueller	2022-04-09
3d81135	jens-daniel-mueller	2022-04-07
a74e341	jens-daniel-mueller	2022-04-04
5a6be34	jens-daniel-mueller	2022-03-22
bd9e11d	jens-daniel-mueller	2022-03-22
2501978	jens-daniel-mueller	2022-03-21
251c7cf	jens-daniel-mueller	2022-02-17
565224d	jens-daniel-mueller	2022-02-17
2116dd3	jens-daniel-mueller	2022-02-09
6fe70a1	jens-daniel-mueller	2022-02-05
a6b33aa	jens-daniel-mueller	2022-02-04
d2191ad	jens-daniel-mueller	2022-02-04
0d0f790	jens-daniel-mueller	2022-02-02
60727e6	jens-daniel-mueller	2022-02-02
c7b4984	jens-daniel-mueller	2022-02-02
7fb28a2	jens-daniel-mueller	2022-02-02
7655085	jens-daniel-mueller	2022-02-02
ec7fe7e	jens-daniel-mueller	2022-01-31

ggsave(path = here::here("output/publication"),
       filename = "FigS_dcant_budget_global_vs_atm_pCO2.png",
       height = 6,
       width = 7)

# p_tcant_year_trans / p_tcant_atm_co2
# ggsave(path = here::here("output/publication"),
#        filename = "Fig_global_dcant_budget_vs_atm_pCO2_trans.png",
#        height = 6,
#        width = 7)

tcant_ts %>% 
  mutate(beta = dcant_mean / (pCO2-lag(pCO2)))

# A tibble: 4 × 8
   year dcant_mean dcant_sd  pCO2 tcant tcant_sd dcant_sd_int  beta
  <dbl>      <dbl>    <dbl> <dbl> <dbl>    <dbl>        <dbl> <dbl>
1  1800        0      NA     280     0       0          NA    NA   
2  1994      118       0     358.  118      19          19     1.51
3  2004       26.8     4.80  377.  145.     19.6         4.80  1.44
4  2014       27.5     4.97  397.  172.     19.6         4.97  1.35

11.2 incl fluxes

Ocean_Sink <- Ocean_Sink %>%
  filter(product != "Watson")

Ocean_Sink_cum_1994 <-
  Ocean_Sink %>%
  filter(year >= 1994, year <= 2007) %>%
  mutate(fgco2_glob = if_else(year == 1994, 0, GtC_yr),
         fgco2_glob = fgco2_glob) %>% 
  arrange(year) %>%
  group_by(product, type) %>%
  mutate(fgco2_glob_cum = cumsum(fgco2_glob)) %>%
  ungroup()

Ocean_Sink_cum_1994 <-
  left_join(Ocean_Sink_cum_1994,
            co2_atm) 

Ocean_Sink_cum_1994 %>%
  ggplot(aes(pCO2, fgco2_glob_cum, col = product)) +
  geom_line() +
  geom_point(shape = 21, fill = "white") +
  theme(legend.position = "bottom") +
  facet_grid(.~ type)

Past versions of GCB_carbon_sink_cum_1994_2007_reference-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26

Ocean_Sink_cum_1994 <- Ocean_Sink_cum_1994 %>% 
  mutate(fgco2_glob_cum_total = fgco2_glob_cum + 118) %>%
  select(year, pCO2, product, fgco2_glob_cum, fgco2_glob_cum_total, type)

Ocean_Sink_cum_1994 <- Ocean_Sink_cum_1994 %>% 
  mutate(type = case_when(
    type == "data_products" ~ "Observation-based\nsurface flux\nproducts",
    TRUE ~ "Global Ocean\nBiogeochemical Models\n(GOBM)"
  ))

Ocean_Sink_cum_1994 %>% 
  filter(year == 2007) %>% 
  group_by(type) %>% 
  summarise(dcant_sd = sd(fgco2_glob_cum)*2) %>% 
  ungroup()

# A tibble: 2 × 2
  type                                          dcant_sd
  <chr>                                            <dbl>
1 "Global Ocean\nBiogeochemical Models\n(GOBM)"     6.99
2 "Observation-based\nsurface flux\nproducts"       6.63

Ocean_Sink_cum_1994 <-
  Ocean_Sink %>%
  filter(year >= 1994, year <= 2014) %>%
  mutate(fgco2_glob = if_else(year == 1994, 0, GtC_yr),
         fgco2_glob = fgco2_glob) %>% 
  arrange(year) %>%
  group_by(product, type) %>%
  mutate(fgco2_glob_cum = cumsum(fgco2_glob)) %>%
  ungroup()

Ocean_Sink_cum_1994 <-
  left_join(Ocean_Sink_cum_1994,
            co2_atm) 

Ocean_Sink_cum_1994 %>%
  ggplot(aes(pCO2, fgco2_glob_cum, col = product)) +
  geom_line() +
  geom_point(shape = 21, fill = "white") +
  theme(legend.position = "bottom") +
  facet_grid(.~ type)

Past versions of GCB_carbon_sink_cum_1994-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
bd24a0f	jens-daniel-mueller	2022-07-15

Ocean_Sink_cum_1994 <- Ocean_Sink_cum_1994 %>% 
  mutate(fgco2_glob_cum_total = fgco2_glob_cum + 118) %>%
  select(year, pCO2, product, fgco2_glob_cum, fgco2_glob_cum_total, type)

Ocean_Sink_cum_1994 <- Ocean_Sink_cum_1994 %>% 
  mutate(type = case_when(
    type == "data_products" ~ "Observation-based\nsurface flux\nproducts",
    TRUE ~ "Global Ocean\nBiogeochemical Models\n(GOBM)"
  ))

tcant_2004 <- tcant_ts %>% 
  filter(year == 2004) %>% 
  pull(tcant)

Ocean_Sink_cum_2004 <-
  Ocean_Sink %>%
  filter(year >= 2004, year <= 2014) %>%
  mutate(fgco2_glob = if_else(year == 2004, 0, GtC_yr)) %>% 
  arrange(year) %>%
  group_by(product, type) %>%
  mutate(fgco2_glob_cum = cumsum(fgco2_glob)) %>%
  ungroup()

Ocean_Sink_cum_2004 <-
  left_join(Ocean_Sink_cum_2004,
            co2_atm) 

Ocean_Sink_cum_2004 %>%
  ggplot(aes(pCO2, fgco2_glob_cum, col = product)) +
  geom_line() +
  geom_point(shape = 21, fill = "white") +
  theme(legend.position = "bottom") +
  facet_grid(.~ type)

Past versions of GCB_carbon_sink_cum_2004-1.png

Version	Author	Date
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
bd24a0f	jens-daniel-mueller	2022-07-15

Ocean_Sink_cum_2004 <- Ocean_Sink_cum_2004 %>% 
  mutate(fgco2_glob_cum_total = fgco2_glob_cum + tcant_2004) %>% 
  select(year, pCO2, product, fgco2_glob_cum, fgco2_glob_cum_total, type)

Ocean_Sink_cum_2004 <- Ocean_Sink_cum_2004 %>% 
  mutate(type = case_when(
    type == "data_products" ~ "Observation-based\nsurface flux\nproducts",
    TRUE ~ "Global Ocean\nBiogeochemical Models\n(GOBM)"
  ))

tcant_ts_zoom <- tcant_ts %>%
  filter(year != 1800)

tcant_min = tcant_ts_zoom %>% pull(tcant) %>% min()
tcant_max =
  tcant_ts_zoom %>% pull(tcant) %>% max() +
  tcant_ts_zoom %>% pull(dcant_sd) %>% max() + 0.2


p_tcant_ts_zoom <-
  tcant_ts_zoom %>%
  ggplot(aes(pCO2, tcant)) +
  geom_ribbon(
    aes(ymin = tcant - dcant_sd,
        ymax = tcant + dcant_sd),
    alpha = 0.2,
    fill = "#BB5566"
  ) +
  geom_ribbon(
    aes(
      ymin = tcant - dcant_sd / 2,
      ymax = tcant + dcant_sd / 2
    ),
    alpha = 0.3,
    fill = "#BB5566"
  ) +
  geom_line(data = Ocean_Sink_cum_1994,
            aes(pCO2, fgco2_glob_cum_total, group = product, col = type)) +
  geom_point(
    data = Ocean_Sink_cum_1994,
    aes(pCO2, fgco2_glob_cum_total, group = product, col = type),
    size = 0.3
  ) +
  geom_point(
    data = Ocean_Sink_cum_1994 %>% filter(year %in% c(2004, 2014)),
    aes(
      pCO2,
      fgco2_glob_cum_total,
      group = product,
      col = type
    ),
    fill = "white",
    shape = 21
  ) +
  geom_line(aes(col = "eMLR(C*)")) +
  geom_point(aes(col = "eMLR(C*)"),
             shape = 21,
             fill = "white") +
  geom_text(aes(label = year), nudge_x = -2, nudge_y = 3) +
  scale_color_highcontrast(name = "Data source", reverse = TRUE) +
  scale_fill_highcontrast(name = "Data source", reverse = TRUE) +
  scale_y_continuous(limits = c(tcant_min, tcant_max)) +
  labs(x = expression(Atmospheric ~ pCO[2] ~ (µatm)),
       y = expression(Total ~ oceanic ~ carbon ~ accumulation ~ since ~ 1800 ~ (PgC))) +
  guides(colour = guide_legend(order = 1),
         fill = "none") +
  theme_classic() +
  theme(
    legend.background = element_rect(fill = "transparent"),
    legend.position = c(0.25, 0.83),
    legend.title = element_blank(),
    legend.box.just = "top",
    legend.box = "horizontal",
    legend.key.size = unit(2.5, 'lines'),
    panel.grid.major.y = element_line(colour = "black",
                                      size = 0.1)
  )

# p_tcant_ts_zoom

dcant_type_box <-
  bind_rows(
    dcant_budget_global_all_ensemble %>%
      filter(data_source == "obs") %>%
      select(period, dcant) %>%
      mutate(type = "eMLR(C*)",
             dcant = dcant + 118,
             dcant = if_else(period == "2004 - 2014",
                             dcant + tcant_2004 - 118,
                             dcant)),
    Ocean_Sink_cum_1994 %>%
      filter(year %in% c(2004, 2014)) %>%
      mutate(period = if_else(year == 2004,
                              "1994 - 2004",
                              "1994 - 2014")) %>%
      select(period, dcant = fgco2_glob_cum_total, type),
    Ocean_Sink_cum_2004 %>%
      filter(year %in% c(2014)) %>%
      mutate(period = if_else(year == 2014,
                              "2004 - 2014",
                              "other")) %>%
      select(period, dcant = fgco2_glob_cum_total, type)
  )

dcant_type_box_stat <- dcant_type_box %>% 
  group_by(period, type) %>% 
  summarise(dcant_mean = mean(dcant),
            dcant_sd = sd(dcant)) %>% 
  ungroup()

dcant_type_box_stat <-
left_join(
  dcant_type_box_stat,
  dcant_budget_global_all %>%
    filter(data_source == "obs") %>%
    mutate(
      dcant = dcant + 118,
      dcant = if_else(period == "2004 - 2014",
                      dcant + tcant_2004 - 118,
                      dcant)
    ) %>%
    select(period, dcant_st_case = dcant)
)

dcant_type_box_stat <- dcant_type_box_stat %>% 
  mutate(dcant_mean = if_else(type == "eMLR(C*)",
                              dcant_st_case,
                              dcant_mean)) %>% 
  select(-dcant_st_case)

p_tcant_box <-
dcant_type_box %>%
  filter(period == "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
 geom_crossbar(
    data = dcant_type_box_stat %>%
      filter(period == "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(
      x = period,
      y = dcant_mean,
      ymin = dcant_mean - dcant_sd,
      ymax = dcant_mean + dcant_sd,
      col = type,
      fill = type
    ), width = 0.7, alpha = 0.4
  ) +
  geom_crossbar(
    data = dcant_type_box_stat %>%
      filter(period == "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(
      x = period,
      y = dcant_mean,
      ymin = dcant_mean - dcant_sd*2,
      ymax = dcant_mean + dcant_sd*2,
      col = type,
      fill = type
    ), width = 0.7, alpha = 0.2
  ) +
  geom_point(aes(fill = type),
              position = position_jitterdodge(
                dodge.width = 1,
                jitter.width = 0.1),
              shape = 21,
              alpha = 0.5) +
  scale_fill_highcontrast(
    reverse = TRUE,
    name = "Data source",
    guide = "none"
  ) +
  scale_color_highcontrast(
    reverse = TRUE,
    name = "Data source",
    guide = "none"
  ) +
  scale_y_continuous(limits = c(tcant_min, tcant_max)) +
  labs(x = "20yr period") +
  theme_classic() +
  theme(
    axis.line.y.left = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major.y = element_line(colour = "black",
                                      size = 0.1)
  )

p_tcant_box_10 <-
dcant_type_box %>%
  filter(period != "1994 - 2014") %>%
  ggplot(aes(period, dcant)) +
  geom_crossbar(
    data = dcant_type_box_stat %>%
      filter(period != "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(
      x = period,
      y = dcant_mean,
      ymin = dcant_mean - dcant_sd,
      ymax = dcant_mean + dcant_sd,
      col = type,
      fill = type
    ), width = 0.7, alpha = 0.4
  ) +
  geom_crossbar(
    data = dcant_type_box_stat %>%
      filter(period != "1994 - 2014"),
    position = position_dodge(width = 1),
    aes(
      x = period,
      y = dcant_mean,
      ymin = dcant_mean - dcant_sd*2,
      ymax = dcant_mean + dcant_sd*2,
      col = type,
      fill = type
    ), width = 0.7, alpha = 0.2
  ) +
  geom_point(aes(fill = type),
              position = position_jitterdodge(
                dodge.width = 1,
                jitter.width = 0.1),
              shape = 21,
              alpha = 0.5) +
  scale_fill_highcontrast(
    reverse = TRUE,
    name = "Data source",
    guide = "none"
  ) +
  scale_color_highcontrast(
    reverse = TRUE,
    name = "Data source",
    guide = "none"
  ) +
  scale_y_continuous(limits = c(tcant_min, tcant_max)) +
  labs(x = "10yr periods") +
  theme_classic() +
  theme(
    axis.line.y.left = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    panel.grid.major.y = element_line(colour = "black",
                                      size = 0.1)
  )

p_tcant_ts_zoom + p_tcant_box + p_tcant_box_10 +
  plot_layout(ncol = 3, widths = c(6,1,1.8)) + 
  plot_annotation(tag_levels = 'A')

Past versions of mean_tcant_over_atm_co2_incl_fluxes_boxplot-1.png

Version	Author	Date
318fefe	jens-daniel-mueller	2022-08-11
5b77c4f	jens-daniel-mueller	2022-08-10
9c3be27	jens-daniel-mueller	2022-08-09
a691b29	jens-daniel-mueller	2022-08-09
910f96e	jens-daniel-mueller	2022-08-08
f68fcfa	jens-daniel-mueller	2022-07-26
f2615fb	jens-daniel-mueller	2022-07-26
4af29f9	jens-daniel-mueller	2022-07-24
b18b250	jens-daniel-mueller	2022-07-20
a80b59b	jens-daniel-mueller	2022-07-20
fea41c1	jens-daniel-mueller	2022-07-20
d803308	jens-daniel-mueller	2022-07-19
b1f7ab3	jens-daniel-mueller	2022-07-18
d2ae54c	jens-daniel-mueller	2022-07-18
2695085	jens-daniel-mueller	2022-07-17
535196a	jens-daniel-mueller	2022-07-17
d20faeb	jens-daniel-mueller	2022-07-17
afb27ad	jens-daniel-mueller	2022-07-15
b492b46	jens-daniel-mueller	2022-07-15
bd24a0f	jens-daniel-mueller	2022-07-15
17cd1d1	jens-daniel-mueller	2022-07-13
26e9496	jens-daniel-mueller	2022-07-12
8fb595c	jens-daniel-mueller	2022-07-12
003b161	jens-daniel-mueller	2022-07-12
8ab4a87	jens-daniel-mueller	2022-06-29
7629c78	jens-daniel-mueller	2022-06-29
f6786c8	jens-daniel-mueller	2022-06-29
9393c07	jens-daniel-mueller	2022-06-28
0825298	jens-daniel-mueller	2022-06-28

ggsave(path = here::here("output/publication"),
       filename = "Fig_dcant_budget_global_vs_GCB_ocean_sink.png",
       height = 6,
       width = 9)

dcant_type_significance <- dcant_type_box_stat %>%
  mutate(dcant_mean = dcant_mean - 118,
         dcant_mean = if_else(period == "2004 - 2014",
                         dcant_mean - (tcant_2004 - 118),
                         dcant_mean),
         dcant_sd = dcant_sd * 2) %>%
  group_by(period) %>%
  mutate(
    dcant_diff = dcant_mean - first(dcant_mean),
    dcant_diff_rel = 100 * dcant_diff / first(dcant_mean),
    dcant_diff_sd = sqrt(dcant_sd ^ 2 + first(dcant_sd) ^ 2),
    dcant_diff_sd_comparison = dcant_diff_sd - abs(dcant_diff)
  ) %>%
  ungroup()

dcant_type_significance %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

period	type	dcant_mean	dcant_sd	dcant_diff	dcant_diff_rel	dcant_diff_sd	dcant_diff_sd_comparison
1994 - 2004	eMLR(C*)	26.79321	4.799487	0.000000	0.000000	6.787499	6.787499
1994 - 2004	Global Ocean Biogeochemical Models (GOBM)	19.52578	5.403004	-7.267428	-27.124143	7.226862	-0.040566
1994 - 2004	Observation-based surface flux products	21.71168	5.448186	-5.081530	-18.965740	7.260703	2.179173
1994 - 2014	eMLR(C*)	54.26581	4.972849	0.000000	0.000000	7.032670	7.032670
1994 - 2014	Global Ocean Biogeochemical Models (GOBM)	42.28477	10.974456	-11.981043	-22.078436	12.048564	0.067521
1994 - 2014	Observation-based surface flux products	48.80475	8.824335	-5.461062	-10.063539	10.129073	4.668011
2004 - 2014	eMLR(C*)	27.52167	3.319445	0.000000	0.000000	4.694404	4.694404
2004 - 2014	Global Ocean Biogeochemical Models (GOBM)	22.75899	5.576053	-4.762678	-17.305191	6.489305	1.726627
2004 - 2014	Observation-based surface flux products	27.09308	4.225215	-0.428594	-1.557297	5.373188	4.944594

12 Revelle changes

delta_revelle <- co2_atm %>%
  mutate(revelle = seacarb::buffer(24,
                                   pCO2,
                                   2300 * 1e-6)$BetaD,
         DIC = seacarb::carb(24,
                                   pCO2,
                                   2300 * 1e-6)$DIC,
         delta_DIC = (1/pCO2) * DIC * (1/revelle) *1e6,
         delta_DIC_rel = delta_DIC / mean(delta_DIC),
         delta_DIC_rel_cum = delta_DIC_rel - first(delta_DIC_rel),
         delta_year = year - first(year))
  
  
delta_revelle %>% 
  ggplot(aes(delta_year,delta_DIC_rel_cum)) +
  geom_smooth(method = "lm", aes(col="lm"), se = FALSE) +
  geom_path() +
  geom_point()

Past versions of revelle_changes-1.png

Version	Author	Date
fea41c1	jens-daniel-mueller	2022-07-20

min(delta_revelle$delta_DIC_rel_cum / max(delta_revelle$delta_year))

[1] -0.006078176

13 Emission removal

GCB_Cant_emissions <- Global_Carbon_Budget %>%
  mutate(year = cut(year, c(1994, 2004, 2014), c("2004", "2014")),
         year = as.numeric(as.character(year))) %>%
  drop_na() %>%
  filter(estimate %in% c(
    "fossil emissions excluding carbonation",
    "land-use change emissions"
  )) %>%
  group_by(year) %>%
  summarise(cant_emissions = sum(GtC_yr)) %>%
  ungroup()

left_join(GCB_Cant_emissions,
          tcant_ts_zoom) %>%
  mutate(
    dcant_per_emission = 100 * dcant_mean / cant_emissions,
    dcant_per_emission_sd = 100 * dcant_sd / cant_emissions
  ) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(height = "300px")

year	cant_emissions	dcant_mean	dcant_sd	pCO2	tcant	tcant_sd	dcant_sd_int	dcant_per_emission	dcant_per_emission_sd
2004	82.66365	26.79321	4.799487	376.95	144.7932	19.59681	4.799487	32.41232	5.806042
2014	101.81399	27.52167	4.972849	397.34	172.3149	19.63999	4.972849	27.03132	4.884249

13.0.0.1 Map test color palettes

breaks <- c(-Inf, seq(0, 16, 2), Inf)

legend_title <- expression(atop(Delta * C["ant"],
                                (mol ~ m ^ {
                                  -2
                                })))

breaks_n <- length(breaks) - 1

dcant_inv_all_color_test <- dcant_inv_all %>%
  filter(data_source %in% c("obs"),
         period == "2004 - 2014") %>%
  mutate(dcant_int = cut(dcant,
                         breaks,
                         right = FALSE))

scico_continous_palettes <- c(
  "acton",
  "bamako",
  "batlow",
  "bilbao",
  "buda",
  "davos",
  "devon",
  "grayC",
  "hawaii",
  "imola",
  "lajolla",
  "lapaz",
  "nuuk",
  "oslo",
  "tokyo",
  "turku"
)

p_test_scico <- function(df, i_palette) {
  p_reg <- map +
    geom_raster(data = df,
                aes(lon, lat, fill = dcant_int)) +
    scale_fill_scico_d(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      guide = "none"
    ) +
    labs(title = i_palette) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_rev <- map +
    geom_raster(data = df,
              aes(lon, lat, fill = dcant_int)) +
    scale_fill_scico_d(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      direction = -1,
      guide = "none"
    ) +
    labs(title = paste(i_palette, "rev")) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_comb <- p_reg / p_rev
  
  return(p_comb)
}


all_plots_scico <- scico_continous_palettes %>% 
  # head(1) %>% 
  map(~p_test_scico(df = dcant_inv_all_color_test,
                      i_palette = .x))

viridis_continous_palettes <- c(
  "viridis",
  "magma",
  "inferno",
  "plasma"
)

p_test_viridis <- function(df, i_palette) {
  p_reg <- map +
    geom_raster(data = df,
                aes(lon, lat, fill = dcant_int)) +
    scale_fill_viridis_d(
      option = i_palette,
      drop = FALSE,
      name = legend_title,
      guide = "none"
    ) +
    labs(title = i_palette) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_rev <- map +
    geom_raster(data = df,
              aes(lon, lat, fill = dcant_int)) +
    scale_fill_viridis_d(
      option = i_palette,
      drop = FALSE,
      name = legend_title,
      direction = -1,
      guide = "none"
    ) +
    labs(title = paste(i_palette, "rev")) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_comb <- p_reg / p_rev
  
  return(p_comb)
}

all_plots_viridis <- viridis_continous_palettes %>% 
  # head(1) %>% 
  map(~p_test_viridis(df = dcant_inv_all_color_test,
                      i_palette = .x))



library(colorspace)

colorspace_continous_palettes <- c(
  # "Purple - Blue",
  # "Red - Purple",
  # "Blue - Yellow",
  # "Heat",
  # "Heat 2",
  "Rocket"
  # "Mako",
  # "Dark",
  # "Mint",
  # "Mint",
  # "BluGrn",
  # "Teal",
  # "Sunset",
  # "SunsetDark",
  # "ag_Sunset",
  # "YlOrRd",
  # "YlGnBu",
  # "RdPu"
)

p_test_colorspace <- function(df, i_palette) {
  
  i_palette <<- i_palette
  
  p_reg <- map +
    geom_raster(data = df,
                aes(lon, lat, fill = dcant_int)) +
    scale_fill_discrete_sequential(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      guide = "none"
    ) +
    labs(title = i_palette) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())
  
  p_rev <- map +
    geom_raster(data = df,
                aes(lon, lat, fill = dcant_int)) +
    scale_fill_discrete_sequential(
      palette = i_palette,
      drop = FALSE,
      name = legend_title,
      rev = FALSE,
      guide = "none"
    ) +
    labs(title = paste(i_palette, "rev")) +
    theme(axis.text = element_blank(),
          axis.ticks = element_blank())

  p_comb <- p_reg / p_rev
  
  return(p_comb)
  rm(i_palette)
  
}

all_plots_colorspace <- colorspace_continous_palettes %>%
  # head(2) %>%
  map(~p_test_colorspace(df = dcant_inv_all_color_test,
                           i_palette = .x))

all_plots_colorspace

all_plots <- c(all_plots_colorspace, all_plots_viridis, all_plots_scico)

pdf(here::here("output/publication/test_color_scales.pdf"),
    width = 4, height = 4)
all_plots
dev.off()

Session information

sessionInfo()

R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.3

Matrix products: default
BLAS:   /usr/local/R-4.1.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.1.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggh4x_0.2.1        khroma_1.9.0       BSDA_1.2.1         lattice_0.20-45   
 [5] kableExtra_1.3.4   ggdist_3.1.1       ggalluvial_0.12.3  geomtextpath_0.1.0
 [9] colorspace_2.0-2   marelac_2.1.10     shape_1.4.6        ggforce_0.3.3     
[13] metR_0.11.0        scico_1.3.0        patchwork_1.1.1    collapse_1.7.0    
[17] forcats_0.5.1      stringr_1.4.0      dplyr_1.0.7        purrr_0.3.4       
[21] readr_2.1.1        tidyr_1.1.4        tibble_3.1.6       ggplot2_3.3.5     
[25] tidyverse_1.3.1    workflowr_1.7.0   

loaded via a namespace (and not attached):
  [1] readxl_1.3.1         backports_1.4.1      Hmisc_4.6-0         
  [4] systemfonts_1.0.3    splines_4.1.2        digest_0.6.29       
  [7] htmltools_0.5.2      fansi_1.0.2          magrittr_2.0.1      
 [10] checkmate_2.0.0      cluster_2.1.2        tzdb_0.2.0          
 [13] modelr_0.1.8         vroom_1.5.7          svglite_2.0.0       
 [16] jpeg_0.1-9           rvest_1.0.2          ggrepel_0.9.1       
 [19] textshaping_0.3.6    haven_2.4.3          xfun_0.29           
 [22] callr_3.7.0          crayon_1.4.2         jsonlite_1.7.3      
 [25] survival_3.2-13      zoo_1.8-9            glue_1.6.0          
 [28] polyclip_1.10-0      gtable_0.3.0         webshot_0.5.2       
 [31] distributional_0.3.0 scales_1.1.1         DBI_1.1.2           
 [34] Rcpp_1.0.8           isoband_0.2.5        viridisLite_0.4.0   
 [37] htmlTable_2.4.0      foreign_0.8-82       bit_4.0.4           
 [40] proxy_0.4-26         Formula_1.2-4        htmlwidgets_1.5.4   
 [43] httr_1.4.2           seacarb_3.3.0        RColorBrewer_1.1-2  
 [46] ellipsis_0.3.2       pkgconfig_2.0.3      farver_2.1.0        
 [49] nnet_7.3-17          sass_0.4.0           dbplyr_2.1.1        
 [52] utf8_1.2.2           here_1.0.1           tidyselect_1.1.1    
 [55] labeling_0.4.2       rlang_1.0.2          later_1.3.0         
 [58] munsell_0.5.0        cellranger_1.1.0     tools_4.1.2         
 [61] cli_3.1.1            generics_0.1.1       broom_0.7.11        
 [64] evaluate_0.14        fastmap_1.1.0        yaml_2.2.1          
 [67] oce_1.5-0            processx_3.5.2       knitr_1.37          
 [70] bit64_4.0.5          fs_1.5.2             nlme_3.1-155        
 [73] whisker_0.4          xml2_1.3.3           compiler_4.1.2      
 [76] rstudioapi_0.13      png_0.1-7            e1071_1.7-9         
 [79] reprex_2.0.1         tweenr_1.0.2         bslib_0.3.1         
 [82] stringi_1.7.6        highr_0.9            ps_1.6.0            
 [85] Matrix_1.4-0         vctrs_0.3.8          pillar_1.6.4        
 [88] lifecycle_1.0.1      jquerylib_0.1.4      gsw_1.0-6           
 [91] data.table_1.14.2    httpuv_1.6.5         R6_2.5.1            
 [94] latticeExtra_0.6-29  promises_1.2.0.1     gridExtra_2.3       
 [97] MASS_7.3-55          assertthat_0.2.1     rprojroot_2.0.2     
[100] withr_2.4.3          SolveSAPHE_2.1.0     mgcv_1.8-38         
[103] parallel_4.1.2       hms_1.1.1            grid_4.1.2          
[106] rpart_4.1-15         class_7.3-20         rmarkdown_2.11      
[109] git2r_0.29.0         getPass_0.2-2        lubridate_1.8.0     
[112] base64enc_0.1-3